diff --git a/src/planner/ACKERMANN_HYBRID_ASTAR_PLAN.md b/src/planner/ACKERMANN_HYBRID_ASTAR_PLAN.md
index 9e943a3..bdd958e 100644
--- a/src/planner/ACKERMANN_HYBRID_ASTAR_PLAN.md
+++ b/src/planner/ACKERMANN_HYBRID_ASTAR_PLAN.md
@@ -43,7 +43,7 @@ topology_pure_pursuit
/cmd_vel
|
v
-cmd_vel_to_ackermann_drive.py
+ackermann_cmd_bridge.py
|
v
/ackermann_cmd
@@ -385,7 +385,7 @@ reverse_speed: 0.12
/cmd_vel.linear.x < 0
```
-之后由现有 `cmd_vel_to_ackermann_drive.py` 桥接到 `/ackermann_cmd`。
+实车启动时由 `planner/scripts/ackermann_cmd_bridge.py` 桥接到 `/ackermann_cmd`。
## 12. 启动方式
diff --git a/src/planner/CMakeLists.txt b/src/planner/CMakeLists.txt
index eec7524..0e65f0d 100644
--- a/src/planner/CMakeLists.txt
+++ b/src/planner/CMakeLists.txt
@@ -1,14 +1,14 @@
-cmake_minimum_required(VERSION 3.5)
-project(planner)
-
-if(NOT CMAKE_CXX_STANDARD)
- set(CMAKE_CXX_STANDARD 17)
-endif()
-
-if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
- add_compile_options(-Wall -Wextra)
-endif()
-
+cmake_minimum_required(VERSION 3.5)
+project(planner)
+
+if(NOT CMAKE_CXX_STANDARD)
+ set(CMAKE_CXX_STANDARD 17)
+endif()
+
+if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+ add_compile_options(-Wall -Wextra)
+endif()
+
find_package(ament_cmake REQUIRED)
find_package(geometry_msgs REQUIRED)
find_package(nav_msgs REQUIRED)
@@ -18,7 +18,6 @@ find_package(tf2 REQUIRED)
find_package(tf2_geometry_msgs REQUIRED)
find_package(tf2_ros REQUIRED)
-# Dummy executable to satisfy ament_cmake + install of launch/config
add_executable(planner_version src/planner_version.cpp)
add_executable(grid_astar_theta_node src/grid_astar_theta_node.cpp)
ament_target_dependencies(grid_astar_theta_node
@@ -40,24 +39,38 @@ install(TARGETS
install(PROGRAMS
scripts/topology_pure_pursuit_node.py
scripts/grid_astar_theta_node.py
+ scripts/send_goal.py
+ scripts/send_start_goal.py
+ scripts/odom_map_tf.py
+ scripts/preflight_check.py
+ scripts/map_info.py
+ scripts/navigation_test_recorder.py
+ scripts/goal_reach_debug_collector.py
+ scripts/ackermann_cmd_bridge.py
+ scripts/lidar_health_check.py
+ scripts/evaluate_navigation_test.py
DESTINATION lib/${PROJECT_NAME}
)
install(
- DIRECTORY launch config
+ DIRECTORY launch config maps
DESTINATION share/${PROJECT_NAME}
)
install(FILES
README.md
+ ACKERMANN_HYBRID_ASTAR_PLAN.md
+ REAL_ROBOT_RUNBOOK.md
+ REAL_ROBOT_TEST_PLAN_ZH.md
+ 操作手册.md
DESTINATION share/${PROJECT_NAME}
)
if(BUILD_TESTING)
- find_package(ament_lint_auto REQUIRED)
- set(ament_cmake_copyright_FOUND TRUE)
- set(ament_cmake_cpplint_FOUND TRUE)
- ament_lint_auto_find_test_dependencies()
-endif()
-
-ament_package()
+ find_package(ament_lint_auto REQUIRED)
+ set(ament_cmake_copyright_FOUND TRUE)
+ set(ament_cmake_cpplint_FOUND TRUE)
+ ament_lint_auto_find_test_dependencies()
+endif()
+
+ament_package()
diff --git a/src/planner/README.md b/src/planner/README.md
index 699747a..1ba1da3 100644
--- a/src/planner/README.md
+++ b/src/planner/README.md
@@ -102,8 +102,8 @@ treats that segment as reverse and publishes negative `/cmd_vel.linear.x`.
when localization is noisy.
- Tune `lookahead_distance`, `min_turning_radius`, and `reverse_speed` first for
Ackermann corner and reverse behavior.
-- If the base expects Ackermann commands, run the existing
- `cmd_vel_to_ackermann_drive.py` bridge and configure the base driver
+- If the base expects Ackermann commands, run
+ `scripts/ackermann_cmd_bridge.py` and configure the base driver
accordingly.
## Planner Semantics
diff --git a/src/planner/REAL_ROBOT_RUNBOOK.md b/src/planner/REAL_ROBOT_RUNBOOK.md
new file mode 100644
index 0000000..fb0722c
--- /dev/null
+++ b/src/planner/REAL_ROBOT_RUNBOOK.md
@@ -0,0 +1,160 @@
+# Real Robot Custom Hybrid A* Runbook
+
+The dedicated launch file is `real_hybrid_astar.launch.py`. It starts only the
+nodes required by the custom stack:
+
+`origincar_base -> EKF -> AMCL/map_server -> LSLIDAR -> grid_astar_theta_node -> Pure Pursuit -> Ackermann bridge`
+
+Nav2 `controller_server` is intentionally not started. Pure Pursuit is the only
+node publishing `/cmd_vel`.
+
+The repository also contains `planner/maps/real_5x5.yaml`, a synthetic empty
+5x5 m room with boundary walls. Use it to validate the software chain only; it
+must be replaced by a map created from the real environment before driving.
+
+## Confirm before running
+
+1. The map YAML and image are the real map. The repository example `zhihui` map is
+ 109x115 pixels at 0.05 m/pixel, or 5.45x5.75 m, not exactly 5x5 m.
+2. The LSLIDAR model, interface and serial device match the selected parameter
+ file. The example N10 configuration uses `/dev/ttyCH343USB0`.
+3. The STM32 serial device and baud rate are correct. The default is
+ `/dev/ttyACM0` and 115200.
+4. The robot is Ackermann configured and the measured minimum turning radius is
+ close to 0.40 m.
+5. The initial robot pose in the map is known and the start cell is free.
+6. The goal is position-only by default. Set `require_goal_yaw: true` in the
+ planner parameters when a final docking heading is required.
+
+## Build on the robot
+
+```bash
+cd ~/liao_ws
+source /opt/ros/humble/setup.bash
+colcon build --symlink-install \
+ --packages-select \
+ origincar_msg origincar_description origincar_base \
+ lslidar_msgs lslidar_driver planner
+source install/setup.bash
+```
+
+Before launching ROS, validate the map on any machine with Python and PyYAML:
+
+```bash
+python3 src/planner/scripts/map_info.py /actual/map.yaml
+```
+
+The check must report `PASS` for a 5x5 m map. Use `--no-check` only when the
+measured map size is intentional and pass those dimensions to `preflight_check`.
+
+## Start the stack
+
+Use one terminal. Replace the map and lidar parameter paths when necessary.
+
+```bash
+ros2 launch planner real_hybrid_astar.launch.py \
+ map:=$HOME/liao_ws/src/planner/maps/real_5x5.yaml \
+ lidar_params:=$HOME/liao_ws/src/LSLIDAR_X_ROS2-20240228/src/lslidar_driver/params/lidar_uart_ros2/lsn10.yaml \
+ lidar_serial_port:=/dev/ttyCH343USB0 \
+ lidar_model:=N10 \
+ start_lidar:=true \
+ serial_port:=/dev/ttyACM0 \
+ serial_baud:=115200 \
+ enable_motion:=false
+```
+
+The command above uses the repository's synthetic 5x5 m room for a software
+chain test. For real driving, replace `map:=...` with the YAML generated from
+the actual environment and verify its dimensions and origin first.
+
+Set `start_lidar:=false` when a standalone LSLIDAR launch already publishes
+`/scan`; never let two driver processes open the same serial device.
+
+Do not also launch `origincar_bringup`, Nav2 `navigation_launch`, another
+controller, `path_follower_demo.py`, or `udp_to_cmdvel.py`; those can publish a
+competing `/cmd_vel`.
+
+## Verify interfaces
+
+```bash
+ros2 topic echo /scan --once
+ros2 topic echo /map --once
+ros2 topic echo /odom_combined --once
+ros2 run tf2_ros tf2_echo map base_footprint
+ros2 topic echo /hybrid_astar_status
+ros2 topic echo /pure_pursuit_status
+```
+
+AMCL must be initialized before the preflight gate. Publish the measured initial
+pose in the map:
+
+```bash
+ros2 topic pub --once /initialpose geometry_msgs/msg/PoseWithCovarianceStamped \
+ "{header: {frame_id: map}, pose: {pose: {position: {x: 0.54, y: 0.20}, orientation: {w: 1.0}}, covariance: [0.25, 0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.10]}}"
+```
+
+Run the preflight gate before sending any goal. It checks the 5x5m map size,
+`/scan`, `/odom_combined`, `map -> base_footprint`, and that exactly one node
+publishes `/cmd_vel`. It also checks the `/ackermann_cmd` bridge and base
+subscriber:
+
+```bash
+ros2 run planner preflight_check.py
+```
+
+For a deliberately different map size, override the expected dimensions rather
+than ignoring the check:
+
+```bash
+ros2 run planner preflight_check.py --ros-args \
+ -p expected_map_width:=5.45 -p expected_map_height:=5.75
+```
+
+## Send one goal
+
+Start the status and `/plan` monitors in separate terminals before publishing;
+these topics are not transient-local and an observer started later can miss the
+message.
+
+```bash
+ros2 run planner send_goal.py 1.00 1.00
+```
+
+The optional third argument is the final yaw in radians. In the real-robot
+configuration only x/y are required; the planner stops within the position
+tolerance without forcing yaw to zero.
+
+Equivalent raw command:
+
+```bash
+ros2 topic pub --once /goal_pose geometry_msgs/msg/PoseStamped \
+ "{header: {frame_id: map}, pose: {position: {x: 1.00, y: 1.00}, orientation: {w: 1.0}}}"
+```
+
+For the repository map, use a point strictly inside `[-2.5, 2.5)` and clear of
+the inflated boundary walls. The `(1.0, 1.0)` example is valid; `(2.5, 2.5)` is
+outside the usable grid.
+
+The first launch uses `enable_motion:=false`, so it only validates planning.
+After `/plan` and the steering direction have been checked, cancel the goal,
+stop the launch, and restart with `enable_motion:=true` for low-speed driving.
+
+Expected sequence:
+
+1. `/hybrid_astar_status` reports `plan_ok`.
+2. `/plan` contains the generated path.
+3. With `enable_motion:=false`, the robot remains stopped for path inspection.
+4. After restarting with `enable_motion:=true`, `/cmd_vel` is converted to
+ `/ackermann_cmd` and the robot stops within `goal_tolerance` (0.15 m).
+
+Cancel the active goal and stop with:
+
+```bash
+ros2 topic pub --once /navigation_cancel std_msgs/msg/Bool "{data: true}"
+```
+
+This clears the planner goal and tracker path. Publishing one zero `/cmd_vel`
+message is not a reliable stop because the tracker can overwrite it at 20 Hz.
+
+If AMCL localization is not available yet, do not drive autonomously. The
+planner requires a valid `map -> odom` transform and recent `/odom_combined`.
diff --git a/src/planner/REAL_ROBOT_TEST_PLAN_ZH.md b/src/planner/REAL_ROBOT_TEST_PLAN_ZH.md
new file mode 100644
index 0000000..eb9a80e
--- /dev/null
+++ b/src/planner/REAL_ROBOT_TEST_PLAN_ZH.md
@@ -0,0 +1,238 @@
+# Hybrid A* 与 Pure Pursuit 实车测试方案
+
+## 1. 测试目标
+
+分别验证两个对象：
+
+1. Hybrid A* 是否能在规定时间内生成无碰撞、满足最小转弯半径的路径。
+2. Pure Pursuit 是否能稳定跟踪该路径，在到点、取消、定位失效时可靠停车。
+
+测试必须按阶段执行。前一阶段未通过，不进入下一阶段。
+
+## 2. 安全规则
+
+- 至少两人参与：一人操作电脑，一人负责硬件急停。
+- 第一次运动测试必须架起驱动轮。
+- 落地测试速度保持 `linear_speed <= 0.20 m/s`、`reverse_speed <= 0.10 m/s`。
+- 禁止同时启动 Nav2 controller、`origincar_bringup`、巡线、UDP 控制或其他 `/cmd_vel` 发布节点。
+- 合成 `real_5x5.yaml` 只允许做不运动的规划测试。除非真实场地就是对应的 5×5 米空房间，否则禁止用它进行自动驾驶。
+- ROS 取消不是硬件急停。任何异常首先使用硬件急停。
+
+## 3. 测试输出
+
+启动记录器：
+
+```bash
+ros2 run planner navigation_test_recorder.py --ros-args \
+ -p test_name:=P1_straight \
+ -p output_dir:=$HOME/yiliao_ws/test_results
+```
+
+测试结束后按 `Ctrl+C`，得到：
+
+```text
+test_results/P1_straight_时间.csv
+test_results/P1_straight_时间.json
+```
+
+JSON 中的关键指标：
+
+| 字段 | 含义 |
+|---|---|
+| `planning_latency_sec` | 从目标到收到路径的延迟 |
+| `path_occupied_points_max` | 路径落入原始占用/未知栅格的点数 |
+| `minimum_planned_turning_radius_m` | 路径估计最小转弯半径 |
+| `cross_track_error_m` | 实车到当前路径的横向误差 |
+| `final_goal_error_m` | 记录结束时的目标距离 |
+| `cancel_to_zero_command_sec` | 取消到收到零 `/cmd_vel` 的延迟 |
+| `transform_failures` | 记录期间 TF 查询失败次数 |
+
+每个落地用例至少重复 5 次，保留全部 CSV/JSON，不只保留最好结果。
+
+自动评估单个或多个结果：
+
+```bash
+ros2 run planner evaluate_navigation_test.py planning \
+ $HOME/yiliao_ws/test_results/P[1-4]*.json
+
+ros2 run planner evaluate_navigation_test.py tracking \
+ $HOME/yiliao_ws/test_results/T*.json
+```
+
+评估器按本手册的默认阈值返回 `PASS` 或 `FAIL`，退出码分别为 0 或 1。需要调整验收指标时使用脚本的命令行参数，不要修改原始测试结果。
+
+## 4. 阶段 0：硬件与定位门槛
+
+### 4.1 雷达
+
+当前 N10 参数文件默认使用 `/dev/ttyCH343USB0`。先确认：
+
+```bash
+grep -E "lidar_name|interface_selection|serial_port_" \
+ $HOME/yiliao_ws/src/LSLIDAR_X_ROS2-20240228/src/lslidar_driver/params/lidar_uart_ros2/lsn10.yaml
+
+ls -l /dev/ttyACM* /dev/ttyUSB* /dev/ttyCH343USB* 2>/dev/null
+id -nG
+```
+
+任何时刻只允许一个 `lslidar_driver_node`。如果雷达已单独启动，启动 planner 实车入口时使用 `start_lidar:=false`。
+
+启动后检查：
+
+```bash
+ros2 param get /lslidar_driver_node lidar_name
+ros2 param get /lslidar_driver_node interface_selection
+ros2 param get /lslidar_driver_node serial_port_
+ros2 topic hz /scan
+ros2 run planner lidar_health_check.py
+```
+
+通过标准：连续 60 秒有 `/scan`，频率不低于雷达标称频率的 80%，无持续设备断连。
+
+旧版雷达驱动可能反复打印笼统的 `error`。是否阻塞测试以 `/scan` 健康检查为准：健康检查通过时可继续只规划测试；没有 `/scan` 或健康检查失败时必须先处理设备路径、权限、占用或供电问题。
+
+### 4.2 底盘、地图与 TF
+
+```bash
+ros2 topic hz /odom
+ros2 topic hz /odom_combined
+ros2 run tf2_ros tf2_echo odom base_footprint
+ros2 run tf2_ros tf2_echo map base_footprint
+ros2 run planner preflight_check.py
+```
+
+通过标准：
+
+- `/odom_combined` 稳定接近 20 Hz；
+- `odom -> base_footprint` 连续；
+- 设置 AMCL 初始位姿后 `map -> base_footprint` 连续；
+- `preflight_check.py` 返回成功；
+- `/cmd_vel`、`/ackermann_cmd` 的发布/订阅数量均符合预检要求。
+
+## 5. 阶段 1：只规划测试
+
+启动时必须使用：
+
+```text
+enable_motion:=false
+```
+
+启动记录器和三个监视终端：
+
+```bash
+ros2 topic echo /hybrid_astar_status
+ros2 topic echo /pure_pursuit_status
+ros2 topic echo /plan
+```
+
+以测试地图中心附近 `(0, 0, 0)` 为起点，执行以下用例：
+
+| 用例 | 操作 | 预期 |
+|---|---|---|
+| P1 直线路径 | `send_goal.py 1.0 0.0` | 非空路径，无倒车段 |
+| P2 转弯路径 | `send_goal.py 1.0 1.0` | 曲率连续且无占用点 |
+| P3 后方目标 | `send_goal.py -0.8 0.0` | 允许出现倒车段，不频繁换挡 |
+| P4 锥桶重规划 | 路径发布后在路径中部发布锥桶 | 2 Hz 内重规划或安全清空路径 |
+| P5 目标占用 | 把目标放在边界墙上 | 拒绝规划并发布空路径 |
+| P6 取消 | 路径生成后发布取消 | 规划器和跟踪器都报告取消 |
+
+锥桶命令示例：
+
+```bash
+ros2 topic pub --once /random_obstacles \
+ std_msgs/msg/Float32MultiArray "{data: [0.5, 0.0, 0.15]}"
+
+ros2 topic pub --once /clear_random_obstacles \
+ std_msgs/msg/Bool "{data: true}"
+```
+
+规划器通过标准：
+
+- 自由起终点成功率 >= 95%（至少 20 次）；
+- 规划延迟 p95 <= 0.50 秒，最大值 <= 1.0 秒；
+- `path_occupied_points_max == 0`；
+- 规划最小转弯半径 >= 配置值的 90%（当前为 0.36 米）；
+- 目标占用或无路时必须清空危险旧路径；
+- 锥桶更新到新路径/空路径的时间 <= 0.75 秒；
+- `enable_motion:=false` 时底盘不得运动。
+
+## 6. 阶段 2：架空轮控制测试
+
+架起驱动轮并准备硬件急停，然后用 `enable_motion:=true` 重启。重新初始化 AMCL 并运行预检。
+
+依次测试短距离前进、左转、右转、倒车，观察：
+
+```bash
+ros2 topic echo /cmd_vel
+ros2 topic echo /ackermann_cmd
+```
+
+通过标准：
+
+- 正速度对应车轮前进，负速度对应车轮倒转；
+- 正转角和负转角分别对应预期左右方向；
+- `/ackermann_cmd.drive.steering_angle` 不超过 `0.60 rad`；
+- 无命令时不持续转动；
+- 停止 `/cmd_vel` 后，planner 内的桥接看门狗在 0.25 秒左右将 `/ackermann_cmd` 归零；
+- 发布 `/navigation_cancel` 后 0.25 秒内命令归零。
+
+任何符号相反都必须先修正，禁止通过交换测试目标来掩盖问题。
+
+## 7. 阶段 3：低速落地跟踪
+
+必须换成与真实环境匹配的地图。每次用例流程：
+
+1. 将机器人放在标记起点。
+2. 设置 AMCL 初始位姿并等待扫描与地图重合。
+3. 运行 `preflight_check.py`。
+4. 启动记录器。
+5. 发布目标，保持急停人员就位。
+6. 到达或失败后立即取消，停止记录器。
+7. 重复 5 次。
+
+推荐用例：
+
+| 用例 | 路径特征 | 距离 |
+|---|---|---|
+| T1 | 直线 | 1.0 米 |
+| T2 | 单个 90 度转弯 | 1.0 至 1.5 米 |
+| T3 | S 形连续转弯 | 2.0 至 3.0 米 |
+| T4 | 包含倒车 | 0.5 至 1.0 米 |
+| T5 | 锥桶触发重规划 | 1.5 至 2.0 米 |
+
+跟踪器基线通过标准：
+
+- 到点成功率 >= 90%（每类至少 5 次，总计至少 25 次）；
+- 横向误差 RMS <= 0.12 米；
+- 横向误差 p95 <= 0.20 米；
+- 最大横向误差 <= 0.30 米；
+- 最终位置误差 <= 0.15 米；
+- 无碰撞、压线、失控或错误换挡；
+- 定位/里程计过期时能停车；
+- 取消到零命令 p95 <= 0.25 秒；
+- TF 查询失败为 0。
+
+这些是当前 0.20 m/s 低速配置的基线。如果场地测量精度不足，应先改善定位，不应直接放宽安全指标。
+
+## 8. 阶段 4：故障与恢复
+
+| 故障注入 | 合格行为 |
+|---|---|
+| 拔掉雷达或停止 `/scan` | AMCL 不可信，不再发送新目标；人工取消 |
+| 暂停 `/odom_combined` | 跟踪器在 `max_odom_age` 后发布零速度 |
+| 发布路径上的锥桶 | 安全重规划；无安全路径则停车 |
+| 发布 `/navigation_cancel` | 清目标、清路径并停车 |
+| 启动第二个 `/cmd_vel` 发布者 | 预检失败，禁止测试 |
+| 目标在地图外或障碍物内 | 空路径并报告拒绝/失败 |
+
+关键安全项出现一次失败，即本轮版本不合格，修复后从阶段 1 重新回归。
+
+## 9. 最终判定
+
+只有同时满足以下条件，才能认为规划与跟踪方案适合当前低速实车：
+
+1. 阶段 0 全部通过。
+2. 规划成功率、延迟、碰撞和转弯半径全部达标。
+3. 25 次以上落地测试满足成功率与误差指标。
+4. 取消、定位过期和无路场景均能可靠停车。
+5. 没有任何碰撞、失控、错误方向或多控制器竞争。
diff --git a/src/planner/config/odom_hybrid_astar.yaml b/src/planner/config/odom_hybrid_astar.yaml
new file mode 100644
index 0000000..d3c7ce3
--- /dev/null
+++ b/src/planner/config/odom_hybrid_astar.yaml
@@ -0,0 +1,66 @@
+grid_astar_theta_planner:
+ ros__parameters:
+ use_sim_time: false
+
+ # Odom-only real-robot test:
+ # - /odom is the only localization source.
+ # - planner_odom_map_tf publishes map->odom from the provided start pose.
+ # - /map is optional static geometry from nav2_map_server; no lidar/AMCL is used.
+ map_frame: map
+ odom_topic: /odom
+ goal_pose_topic: /goal_pose
+ obstacles_topic: /random_obstacles
+ clear_obstacles_topic: /clear_random_obstacles
+ cancel_topic: /navigation_cancel
+ costmap_topic: /map
+ plan_topic: /plan
+ status_topic: /hybrid_astar_status
+
+ # real_10x10.yaml fallback bounds. When /map arrives, exact map bounds are
+ # applied because fit_map_to_costmap is true.
+ map_min_x: -5.0
+ map_min_y: -5.0
+ map_max_x: 5.0
+ map_max_y: 5.0
+ # A 10 cm planning grid keeps odom-only real-vehicle planning responsive
+ # over the full 10 x 10 m test area. The map remains used for collision
+ # checks at its native resolution.
+ resolution: 0.10
+ fit_map_to_costmap: true
+
+ obstacle_format: xyr
+ default_obstacle_radius: 0.15
+ unknown_obstacle_radius: 0.15
+ accumulate_obstacles: false
+ obstacle_merge_distance: 0.20
+ robot_radius: 0.14
+ localization_error: 0.05
+ latency_margin: 0.03
+ safety_margin: 0.06
+ costmap_occupied_threshold: 50
+ unknown_is_obstacle: true
+ outside_costmap_is_obstacle: true
+ inflate_static_map: true
+
+ min_turning_radius: 0.25
+ yaw_bins: 36
+ primitive_length: 0.12
+ collision_sample_step: 0.03
+ goal_xy_tolerance: 0.06
+ goal_yaw_tolerance: 0.174533
+ require_goal_yaw: false
+ use_grid_heuristic: true
+ heuristic_weight: 1.35
+ goal_yaw_heuristic_weight: 0.0
+ reverse_penalty: 1.5
+ turn_penalty: 0.05
+ gear_change_penalty: 0.25
+ steering_change_penalty: 0.02
+ max_iterations: 200000
+ planning_timeout_ms: 1500.0
+ replan_rate: 2.0
+ plan_on_obstacle_update: true
+ max_odom_age: 0.80
+ tf_timeout: 0.10
+ state_log_period: 1.0
+ search_progress_log_period_ms: 500.0
diff --git a/src/planner/config/odom_pure_pursuit.yaml b/src/planner/config/odom_pure_pursuit.yaml
new file mode 100644
index 0000000..d411b26
--- /dev/null
+++ b/src/planner/config/odom_pure_pursuit.yaml
@@ -0,0 +1,35 @@
+topology_pure_pursuit:
+ ros__parameters:
+ use_sim_time: false
+ enable_topology_planning: false
+ accept_external_plan: true
+ publish_cmd_vel: true
+ stop_without_plan: true
+
+ map_frame: map
+ odom_topic: /odom
+ external_plan_topic: /plan
+ cmd_vel_topic: /planner_cmd_vel
+ status_topic: /pure_pursuit_status
+ cancel_topic: /navigation_cancel
+
+ control_rate: 20.0
+ lookahead_distance: 0.40
+ goal_tolerance: 0.06
+ goal_yaw_tolerance: 0.174533
+ align_goal_yaw: false
+ final_alignment_offset: 0.25
+ final_alignment_speed: 0.06
+ linear_speed: 0.60
+ min_linear_speed: 0.08
+ reverse_speed: 0.25
+ slowdown_distance: 0.35
+ curvature_slowdown_gain: 0.10
+ max_angular_speed: 2.80
+ min_turning_radius: 0.25
+
+ latency_compensation: true
+ max_latency_compensation: 0.15
+ max_odom_age: 0.80
+ tf_timeout: 0.10
+ state_log_period: 1.0
diff --git a/src/planner/config/real_amcl.yaml b/src/planner/config/real_amcl.yaml
new file mode 100644
index 0000000..2272cd8
--- /dev/null
+++ b/src/planner/config/real_amcl.yaml
@@ -0,0 +1,41 @@
+amcl:
+ ros__parameters:
+ use_sim_time: false
+ alpha1: 0.20
+ alpha2: 0.20
+ alpha3: 0.20
+ alpha4: 0.20
+ alpha5: 0.20
+ base_frame_id: base_footprint
+ global_frame_id: map
+ odom_frame_id: odom
+ scan_topic: /scan
+ laser_model_type: likelihood_field
+ laser_min_range: 0.15
+ laser_max_range: 20.0
+ laser_likelihood_max_dist: 2.0
+ max_beams: 60
+ min_particles: 500
+ max_particles: 2000
+ pf_err: 0.05
+ pf_z: 0.99
+ resample_interval: 1
+ robot_model_type: nav2_amcl::DifferentialMotionModel
+ save_pose_rate: 0.5
+ sigma_hit: 0.2
+ tf_broadcast: true
+ transform_tolerance: 0.5
+ update_min_a: 0.15
+ update_min_d: 0.10
+ z_hit: 0.5
+ z_max: 0.05
+ z_rand: 0.5
+ z_short: 0.05
+
+amcl_map_client:
+ ros__parameters:
+ use_sim_time: false
+
+amcl_rclcpp_node:
+ ros__parameters:
+ use_sim_time: false
diff --git a/src/planner/config/real_hybrid_astar.yaml b/src/planner/config/real_hybrid_astar.yaml
new file mode 100644
index 0000000..4ded55e
--- /dev/null
+++ b/src/planner/config/real_hybrid_astar.yaml
@@ -0,0 +1,64 @@
+grid_astar_theta_planner:
+ ros__parameters:
+ use_sim_time: false
+
+ # Real-robot frames and interfaces.
+ map_frame: map
+ odom_topic: /odom_combined
+ goal_pose_topic: /goal_pose
+ obstacles_topic: /random_obstacles
+ clear_obstacles_topic: /clear_random_obstacles
+ cancel_topic: /navigation_cancel
+ # nav2_map_server publishes an OccupancyGrid on /map. The planner can
+ # consume it directly, so a second Nav2 costmap is not required.
+ costmap_topic: /map
+ plan_topic: /plan
+ status_topic: /hybrid_astar_status
+
+ # Fallback is a 5 x 5 m square. When /map arrives, its origin and size are
+ # used because fit_map_to_costmap is enabled.
+ map_min_x: 0.0
+ map_min_y: 0.0
+ map_max_x: 5.0
+ map_max_y: 5.0
+ resolution: 0.05
+ fit_map_to_costmap: true
+
+ obstacle_format: xyr
+ default_obstacle_radius: 0.15
+ unknown_obstacle_radius: 0.15
+ accumulate_obstacles: false
+ obstacle_merge_distance: 0.20
+ robot_radius: 0.14
+ localization_error: 0.05
+ latency_margin: 0.03
+ safety_margin: 0.06
+ costmap_occupied_threshold: 50
+ unknown_is_obstacle: true
+ outside_costmap_is_obstacle: true
+ inflate_static_map: true
+
+ min_turning_radius: 0.40
+ yaw_bins: 72
+ primitive_length: 0.12
+ collision_sample_step: 0.03
+ goal_xy_tolerance: 0.15
+ goal_yaw_tolerance: 0.174533
+ # Point navigation does not require the car to finish at a specific yaw.
+ # Set true when final docking orientation is required.
+ require_goal_yaw: false
+ use_grid_heuristic: true
+ heuristic_weight: 1.25
+ goal_yaw_heuristic_weight: 0.0
+ reverse_penalty: 1.5
+ turn_penalty: 0.05
+ gear_change_penalty: 0.25
+ steering_change_penalty: 0.02
+ max_iterations: 100000
+ planning_timeout_ms: 500.0
+ replan_rate: 2.0
+ plan_on_obstacle_update: true
+ max_odom_age: 0.50
+ tf_timeout: 0.10
+ state_log_period: 1.0
+ search_progress_log_period_ms: 500.0
diff --git a/src/planner/config/real_pure_pursuit.yaml b/src/planner/config/real_pure_pursuit.yaml
new file mode 100644
index 0000000..a479cf2
--- /dev/null
+++ b/src/planner/config/real_pure_pursuit.yaml
@@ -0,0 +1,33 @@
+topology_pure_pursuit:
+ ros__parameters:
+ use_sim_time: false
+ enable_topology_planning: false
+ accept_external_plan: true
+ publish_cmd_vel: true
+ stop_without_plan: true
+
+ map_frame: map
+ odom_topic: /odom_combined
+ external_plan_topic: /plan
+ cmd_vel_topic: /planner_cmd_vel
+ status_topic: /pure_pursuit_status
+ cancel_topic: /navigation_cancel
+
+ control_rate: 20.0
+ lookahead_distance: 0.45
+ goal_tolerance: 0.15
+ goal_yaw_tolerance: 0.174533
+ align_goal_yaw: false
+ linear_speed: 0.20
+ min_linear_speed: 0.06
+ reverse_speed: 0.10
+ slowdown_distance: 0.70
+ curvature_slowdown_gain: 0.25
+ max_angular_speed: 1.20
+ min_turning_radius: 0.40
+
+ latency_compensation: true
+ max_latency_compensation: 0.15
+ max_odom_age: 0.50
+ tf_timeout: 0.10
+ state_log_period: 1.0
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diff --git a/src/planner/launch/odom_hybrid_astar.launch.py b/src/planner/launch/odom_hybrid_astar.launch.py
new file mode 100644
index 0000000..84313e0
--- /dev/null
+++ b/src/planner/launch/odom_hybrid_astar.launch.py
@@ -0,0 +1,394 @@
+#!/usr/bin/env python3
+"""Odom-only bringup for Hybrid A* planning and Pure Pursuit tracking.
+
+This launch does not start lidar or AMCL. The user provides the known current
+map pose; planner_odom_map_tf aligns map->odom once, then localization is
+wheel odom only.
+"""
+
+import os
+
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.conditions import IfCondition
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+from launch_ros.parameter_descriptions import ParameterValue
+
+
+def generate_launch_description():
+ planner_share = get_package_share_directory("planner")
+ description_share = get_package_share_directory("origincar_description")
+
+ map_yaml = LaunchConfiguration("map")
+ use_map = LaunchConfiguration("use_map")
+ start_base = LaunchConfiguration("start_base")
+ start_robot_state_publisher = LaunchConfiguration("start_robot_state_publisher")
+ serial_port = LaunchConfiguration("serial_port")
+ serial_baud = LaunchConfiguration("serial_baud")
+ enable_motion = LaunchConfiguration("enable_motion")
+ wheelbase = LaunchConfiguration("wheelbase")
+ max_steering_angle = LaunchConfiguration("max_steering_angle")
+ map_frame = LaunchConfiguration("map_frame")
+ odom_frame = LaunchConfiguration("odom_frame")
+ base_frame = LaunchConfiguration("base_frame")
+ odom_topic = LaunchConfiguration("odom_topic")
+ initial_x = LaunchConfiguration("initial_x")
+ initial_y = LaunchConfiguration("initial_y")
+ initial_yaw = LaunchConfiguration("initial_yaw")
+ initialize_on_first_odom = LaunchConfiguration("initialize_on_first_odom")
+ planner_min_turning_radius = LaunchConfiguration("planner_min_turning_radius")
+ tracker_min_turning_radius = LaunchConfiguration("tracker_min_turning_radius")
+ lookahead_distance = LaunchConfiguration("lookahead_distance")
+ linear_speed = LaunchConfiguration("linear_speed")
+ min_linear_speed = LaunchConfiguration("min_linear_speed")
+ reverse_speed = LaunchConfiguration("reverse_speed")
+ slowdown_distance = LaunchConfiguration("slowdown_distance")
+ curvature_slowdown_gain = LaunchConfiguration("curvature_slowdown_gain")
+ max_angular_speed = LaunchConfiguration("max_angular_speed")
+ goal_tolerance = LaunchConfiguration("goal_tolerance")
+ goal_yaw_tolerance = LaunchConfiguration("goal_yaw_tolerance")
+ align_goal_yaw = LaunchConfiguration("align_goal_yaw")
+ final_alignment_offset = LaunchConfiguration("final_alignment_offset")
+ final_alignment_speed = LaunchConfiguration("final_alignment_speed")
+ require_goal_yaw = LaunchConfiguration("require_goal_yaw")
+ planner_goal_xy_tolerance = LaunchConfiguration("planner_goal_xy_tolerance")
+ use_grid_heuristic = LaunchConfiguration("use_grid_heuristic")
+ heuristic_weight = LaunchConfiguration("heuristic_weight")
+ goal_yaw_heuristic_weight = LaunchConfiguration("goal_yaw_heuristic_weight")
+ planner_resolution = LaunchConfiguration("planner_resolution")
+ yaw_bins = LaunchConfiguration("yaw_bins")
+ primitive_length = LaunchConfiguration("primitive_length")
+ max_iterations = LaunchConfiguration("max_iterations")
+ planning_timeout_ms = LaunchConfiguration("planning_timeout_ms")
+ state_log_period = LaunchConfiguration("state_log_period")
+ search_progress_log_period_ms = LaunchConfiguration("search_progress_log_period_ms")
+ use_sim_time = LaunchConfiguration("use_sim_time")
+
+ serial_baud_value = ParameterValue(serial_baud, value_type=int)
+ enable_motion_value = ParameterValue(enable_motion, value_type=bool)
+ wheelbase_value = ParameterValue(wheelbase, value_type=float)
+ max_steering_angle_value = ParameterValue(max_steering_angle, value_type=float)
+ initial_x_value = ParameterValue(initial_x, value_type=float)
+ initial_y_value = ParameterValue(initial_y, value_type=float)
+ initial_yaw_value = ParameterValue(initial_yaw, value_type=float)
+ initialize_on_first_odom_value = ParameterValue(
+ initialize_on_first_odom, value_type=bool
+ )
+ planner_min_turning_radius_value = ParameterValue(
+ planner_min_turning_radius, value_type=float
+ )
+ tracker_min_turning_radius_value = ParameterValue(
+ tracker_min_turning_radius, value_type=float
+ )
+ lookahead_distance_value = ParameterValue(lookahead_distance, value_type=float)
+ linear_speed_value = ParameterValue(linear_speed, value_type=float)
+ min_linear_speed_value = ParameterValue(min_linear_speed, value_type=float)
+ reverse_speed_value = ParameterValue(reverse_speed, value_type=float)
+ slowdown_distance_value = ParameterValue(slowdown_distance, value_type=float)
+ curvature_slowdown_gain_value = ParameterValue(
+ curvature_slowdown_gain, value_type=float
+ )
+ max_angular_speed_value = ParameterValue(max_angular_speed, value_type=float)
+ goal_tolerance_value = ParameterValue(goal_tolerance, value_type=float)
+ goal_yaw_tolerance_value = ParameterValue(goal_yaw_tolerance, value_type=float)
+ align_goal_yaw_value = ParameterValue(align_goal_yaw, value_type=bool)
+ final_alignment_offset_value = ParameterValue(
+ final_alignment_offset, value_type=float
+ )
+ final_alignment_speed_value = ParameterValue(
+ final_alignment_speed, value_type=float
+ )
+ require_goal_yaw_value = ParameterValue(require_goal_yaw, value_type=bool)
+ planner_goal_xy_tolerance_value = ParameterValue(
+ planner_goal_xy_tolerance, value_type=float
+ )
+ use_grid_heuristic_value = ParameterValue(use_grid_heuristic, value_type=bool)
+ heuristic_weight_value = ParameterValue(heuristic_weight, value_type=float)
+ goal_yaw_heuristic_weight_value = ParameterValue(
+ goal_yaw_heuristic_weight, value_type=float
+ )
+ planner_resolution_value = ParameterValue(planner_resolution, value_type=float)
+ yaw_bins_value = ParameterValue(yaw_bins, value_type=int)
+ primitive_length_value = ParameterValue(primitive_length, value_type=float)
+ max_iterations_value = ParameterValue(max_iterations, value_type=int)
+ planning_timeout_ms_value = ParameterValue(planning_timeout_ms, value_type=float)
+ state_log_period_value = ParameterValue(state_log_period, value_type=float)
+ search_progress_log_period_ms_value = ParameterValue(
+ search_progress_log_period_ms, value_type=float
+ )
+ use_sim_time_value = ParameterValue(use_sim_time, value_type=bool)
+
+ urdf_path = os.path.join(description_share, "urdf", "origincar.urdf")
+ planner_path = os.path.join(planner_share, "config", "odom_hybrid_astar.yaml")
+ tracker_path = os.path.join(planner_share, "config", "odom_pure_pursuit.yaml")
+
+ robot_state_publisher = Node(
+ package="robot_state_publisher",
+ executable="robot_state_publisher",
+ name="robot_state_publisher",
+ output="screen",
+ condition=IfCondition(start_robot_state_publisher),
+ parameters=[{
+ "robot_description": open(urdf_path, encoding="utf-8").read(),
+ "use_sim_time": use_sim_time_value,
+ }],
+ )
+
+ base_node = Node(
+ package="origincar_base",
+ executable="origincar_base_node",
+ name="origincar_base",
+ output="screen",
+ condition=IfCondition(start_base),
+ parameters=[{
+ "usart_port_name": serial_port,
+ "serial_baud_rate": serial_baud_value,
+ "robot_frame_id": base_frame,
+ "odom_frame_id": odom_frame,
+ "gyro_frame_id": "gyro_link",
+ "cmd_vel": "/planner_disabled_cmd_vel",
+ "akm_cmd_vel": "/ackermann_cmd",
+ "publish_tf": True,
+ "gyro_z_low_pass_alpha": 0.6,
+ }],
+ )
+
+ cmd_vel_bridge = Node(
+ package="planner",
+ executable="ackermann_cmd_bridge.py",
+ name="planner_ackermann_cmd_bridge",
+ output="screen",
+ condition=IfCondition(enable_motion),
+ parameters=[{
+ "input_topic": "/planner_cmd_vel",
+ "output_topic": "/ackermann_cmd",
+ "wheelbase": wheelbase_value,
+ "frame_id": base_frame,
+ "max_steering_angle": max_steering_angle_value,
+ "command_timeout": 0.25,
+ "watchdog_rate": 10.0,
+ "state_log_period": state_log_period_value,
+ }],
+ )
+
+ map_server = Node(
+ package="nav2_map_server",
+ executable="map_server",
+ name="map_server",
+ output="screen",
+ condition=IfCondition(use_map),
+ parameters=[{"yaml_filename": map_yaml, "use_sim_time": use_sim_time_value}],
+ )
+
+ lifecycle_manager = Node(
+ package="nav2_lifecycle_manager",
+ executable="lifecycle_manager",
+ name="lifecycle_manager_odom_map",
+ output="screen",
+ condition=IfCondition(use_map),
+ parameters=[{
+ "use_sim_time": use_sim_time_value,
+ "autostart": True,
+ "node_names": ["map_server"],
+ }],
+ )
+
+ odom_map_tf = Node(
+ package="planner",
+ executable="odom_map_tf.py",
+ name="planner_odom_map_tf",
+ output="screen",
+ parameters=[{
+ "use_sim_time": use_sim_time_value,
+ "map_frame": map_frame,
+ "odom_frame": odom_frame,
+ "odom_topic": odom_topic,
+ "initial_x": initial_x_value,
+ "initial_y": initial_y_value,
+ "initial_yaw": initial_yaw_value,
+ "initialize_on_first_odom": initialize_on_first_odom_value,
+ "state_log_period": state_log_period_value,
+ }],
+ )
+
+ planner_node = Node(
+ package="planner",
+ executable="grid_astar_theta_node",
+ name="grid_astar_theta_planner",
+ output="screen",
+ parameters=[planner_path, {
+ "use_sim_time": use_sim_time_value,
+ "map_frame": map_frame,
+ "odom_topic": odom_topic,
+ "min_turning_radius": planner_min_turning_radius_value,
+ "require_goal_yaw": require_goal_yaw_value,
+ "goal_xy_tolerance": planner_goal_xy_tolerance_value,
+ "use_grid_heuristic": use_grid_heuristic_value,
+ "heuristic_weight": heuristic_weight_value,
+ "goal_yaw_heuristic_weight": goal_yaw_heuristic_weight_value,
+ "resolution": planner_resolution_value,
+ "yaw_bins": yaw_bins_value,
+ "primitive_length": primitive_length_value,
+ "max_iterations": max_iterations_value,
+ "planning_timeout_ms": planning_timeout_ms_value,
+ "state_log_period": state_log_period_value,
+ "search_progress_log_period_ms": search_progress_log_period_ms_value,
+ }],
+ )
+
+ tracker_node = Node(
+ package="planner",
+ executable="topology_pure_pursuit_node.py",
+ name="topology_pure_pursuit",
+ output="screen",
+ parameters=[tracker_path, {
+ "use_sim_time": use_sim_time_value,
+ "map_frame": map_frame,
+ "odom_topic": odom_topic,
+ "publish_cmd_vel": enable_motion_value,
+ "cmd_vel_topic": "/planner_cmd_vel",
+ "min_turning_radius": tracker_min_turning_radius_value,
+ "lookahead_distance": lookahead_distance_value,
+ "linear_speed": linear_speed_value,
+ "min_linear_speed": min_linear_speed_value,
+ "reverse_speed": reverse_speed_value,
+ "slowdown_distance": slowdown_distance_value,
+ "curvature_slowdown_gain": curvature_slowdown_gain_value,
+ "max_angular_speed": max_angular_speed_value,
+ "goal_tolerance": goal_tolerance_value,
+ "goal_yaw_tolerance": goal_yaw_tolerance_value,
+ "align_goal_yaw": align_goal_yaw_value,
+ "final_alignment_offset": final_alignment_offset_value,
+ "final_alignment_speed": final_alignment_speed_value,
+ "state_log_period": state_log_period_value,
+ }],
+ )
+
+ return LaunchDescription([
+ DeclareLaunchArgument(
+ "map",
+ default_value=os.path.join(planner_share, "maps", "real_10x10.yaml"),
+ description="Static map YAML. Used only when use_map=true.",
+ ),
+ DeclareLaunchArgument("use_map", default_value="true"),
+ DeclareLaunchArgument("start_base", default_value="true"),
+ DeclareLaunchArgument("start_robot_state_publisher", default_value="true"),
+ DeclareLaunchArgument("serial_port", default_value="/dev/ttyACM0"),
+ DeclareLaunchArgument("serial_baud", default_value="115200"),
+ DeclareLaunchArgument(
+ "enable_motion",
+ default_value="false",
+ description="Publish motion commands. Keep false for planner-only tests.",
+ ),
+ DeclareLaunchArgument("wheelbase", default_value="0.143"),
+ DeclareLaunchArgument("max_steering_angle", default_value="0.60"),
+ DeclareLaunchArgument("map_frame", default_value="map"),
+ DeclareLaunchArgument("odom_frame", default_value="odom"),
+ DeclareLaunchArgument("base_frame", default_value="base_link"),
+ DeclareLaunchArgument("odom_topic", default_value="/odom"),
+ DeclareLaunchArgument("initial_x", default_value="0.0"),
+ DeclareLaunchArgument("initial_y", default_value="0.0"),
+ DeclareLaunchArgument("initial_yaw", default_value="0.0"),
+ DeclareLaunchArgument(
+ "initialize_on_first_odom",
+ default_value="true",
+ description="Use launch initial pose when the first odom message arrives.",
+ ),
+ DeclareLaunchArgument(
+ "planner_min_turning_radius",
+ default_value="0.25",
+ description="Hybrid A* minimum turning radius in meters.",
+ ),
+ DeclareLaunchArgument(
+ "tracker_min_turning_radius",
+ default_value="0.25",
+ description="Pure Pursuit curvature limit in meters.",
+ ),
+ DeclareLaunchArgument(
+ "lookahead_distance",
+ default_value="0.40",
+ description="Pure Pursuit lookahead in meters.",
+ ),
+ DeclareLaunchArgument("linear_speed", default_value="0.60"),
+ DeclareLaunchArgument("min_linear_speed", default_value="0.08"),
+ DeclareLaunchArgument("reverse_speed", default_value="0.25"),
+ DeclareLaunchArgument("slowdown_distance", default_value="0.35"),
+ DeclareLaunchArgument("curvature_slowdown_gain", default_value="0.10"),
+ DeclareLaunchArgument("max_angular_speed", default_value="2.80"),
+ DeclareLaunchArgument("goal_tolerance", default_value="0.06"),
+ DeclareLaunchArgument("goal_yaw_tolerance", default_value="0.174533"),
+ DeclareLaunchArgument("align_goal_yaw", default_value="false"),
+ DeclareLaunchArgument("final_alignment_offset", default_value="0.25"),
+ DeclareLaunchArgument("final_alignment_speed", default_value="0.06"),
+ DeclareLaunchArgument(
+ "require_goal_yaw",
+ default_value="false",
+ description="Require Hybrid A* to match the goal yaw.",
+ ),
+ DeclareLaunchArgument(
+ "planner_goal_xy_tolerance",
+ default_value="0.06",
+ description="Hybrid A* final XY tolerance in meters.",
+ ),
+ DeclareLaunchArgument(
+ "use_grid_heuristic",
+ default_value="true",
+ description="Use obstacle-aware 2D grid distance as the Hybrid A* heuristic.",
+ ),
+ DeclareLaunchArgument(
+ "heuristic_weight",
+ default_value="1.35",
+ description="Weighted A* heuristic multiplier. Higher is faster but less optimal.",
+ ),
+ DeclareLaunchArgument(
+ "goal_yaw_heuristic_weight",
+ default_value="0.0",
+ description="Weight for final yaw guidance in the Hybrid A* heuristic.",
+ ),
+ DeclareLaunchArgument(
+ "planner_resolution",
+ default_value="0.10",
+ description="Hybrid A* search grid resolution in meters.",
+ ),
+ DeclareLaunchArgument(
+ "yaw_bins",
+ default_value="36",
+ description="Number of discrete Hybrid A* heading states.",
+ ),
+ DeclareLaunchArgument(
+ "primitive_length",
+ default_value="0.12",
+ description="Length of each Hybrid A* motion primitive in meters.",
+ ),
+ DeclareLaunchArgument(
+ "max_iterations",
+ default_value="200000",
+ description="Maximum Hybrid A* state expansions per plan.",
+ ),
+ DeclareLaunchArgument(
+ "planning_timeout_ms",
+ default_value="1500.0",
+ description="Hybrid A* planning time limit in milliseconds.",
+ ),
+ DeclareLaunchArgument(
+ "state_log_period",
+ default_value="1.0",
+ description="Seconds between runtime state logs. Set 0.0 to disable.",
+ ),
+ DeclareLaunchArgument(
+ "search_progress_log_period_ms",
+ default_value="500.0",
+ description="Milliseconds between Hybrid A* search progress logs. Set 0.0 to disable.",
+ ),
+ DeclareLaunchArgument("use_sim_time", default_value="false"),
+ robot_state_publisher,
+ base_node,
+ cmd_vel_bridge,
+ map_server,
+ lifecycle_manager,
+ odom_map_tf,
+ planner_node,
+ tracker_node,
+ ])
diff --git a/src/planner/launch/real_hybrid_astar.launch.py b/src/planner/launch/real_hybrid_astar.launch.py
new file mode 100644
index 0000000..89f8dc0
--- /dev/null
+++ b/src/planner/launch/real_hybrid_astar.launch.py
@@ -0,0 +1,250 @@
+#!/usr/bin/env python3
+"""Real-robot bringup for the custom Hybrid A* and Pure Pursuit stack.
+
+This launch intentionally does not start Nav2 controller_server. The custom
+planner publishes /plan and the tracker publishes an internal Twist command
+that is converted to /ackermann_cmd by planner_ackermann_cmd_bridge.
+"""
+
+import os
+
+from ament_index_python.packages import get_package_share_directory
+from launch import LaunchDescription
+from launch.actions import DeclareLaunchArgument
+from launch.conditions import IfCondition
+from launch.substitutions import LaunchConfiguration
+from launch_ros.actions import Node
+from launch_ros.parameter_descriptions import ParameterValue
+
+
+def generate_launch_description():
+ planner_share = get_package_share_directory("planner")
+ base_share = get_package_share_directory("origincar_base")
+ description_share = get_package_share_directory("origincar_description")
+
+ map_yaml = LaunchConfiguration("map")
+ lidar_params = LaunchConfiguration("lidar_params")
+ lidar_serial_port = LaunchConfiguration("lidar_serial_port")
+ lidar_model = LaunchConfiguration("lidar_model")
+ start_lidar = LaunchConfiguration("start_lidar")
+ serial_port = LaunchConfiguration("serial_port")
+ serial_baud = LaunchConfiguration("serial_baud")
+ enable_motion = LaunchConfiguration("enable_motion")
+ wheelbase = LaunchConfiguration("wheelbase")
+ max_steering_angle = LaunchConfiguration("max_steering_angle")
+ use_grid_heuristic = LaunchConfiguration("use_grid_heuristic")
+ heuristic_weight = LaunchConfiguration("heuristic_weight")
+ goal_yaw_heuristic_weight = LaunchConfiguration("goal_yaw_heuristic_weight")
+ state_log_period = LaunchConfiguration("state_log_period")
+ search_progress_log_period_ms = LaunchConfiguration("search_progress_log_period_ms")
+ use_sim_time = LaunchConfiguration("use_sim_time")
+ serial_baud_value = ParameterValue(serial_baud, value_type=int)
+ enable_motion_value = ParameterValue(enable_motion, value_type=bool)
+ wheelbase_value = ParameterValue(wheelbase, value_type=float)
+ max_steering_angle_value = ParameterValue(max_steering_angle, value_type=float)
+ use_grid_heuristic_value = ParameterValue(use_grid_heuristic, value_type=bool)
+ heuristic_weight_value = ParameterValue(heuristic_weight, value_type=float)
+ goal_yaw_heuristic_weight_value = ParameterValue(
+ goal_yaw_heuristic_weight, value_type=float
+ )
+ state_log_period_value = ParameterValue(state_log_period, value_type=float)
+ search_progress_log_period_ms_value = ParameterValue(
+ search_progress_log_period_ms, value_type=float
+ )
+ use_sim_time_value = ParameterValue(use_sim_time, value_type=bool)
+
+ urdf_path = os.path.join(description_share, "urdf", "origincar.urdf")
+ ekf_path = os.path.join(base_share, "config", "ekf.yaml")
+ amcl_path = os.path.join(planner_share, "config", "real_amcl.yaml")
+ planner_path = os.path.join(planner_share, "config", "real_hybrid_astar.yaml")
+ tracker_path = os.path.join(planner_share, "config", "real_pure_pursuit.yaml")
+
+ # xacro is not needed here because origincar.urdf is already generated and
+ # is the file used by the existing real-robot launch files.
+ robot_state_publisher = Node(
+ package="robot_state_publisher",
+ executable="robot_state_publisher",
+ name="robot_state_publisher",
+ output="screen",
+ parameters=[{"robot_description": open(urdf_path, encoding="utf-8").read(),
+ "use_sim_time": use_sim_time_value}],
+ )
+
+ base_node = Node(
+ package="origincar_base",
+ executable="origincar_base_node",
+ name="origincar_base",
+ output="screen",
+ parameters=[{
+ "usart_port_name": serial_port,
+ "serial_baud_rate": serial_baud_value,
+ "robot_frame_id": "base_link",
+ "odom_frame_id": "odom",
+ "gyro_frame_id": "gyro_link",
+ # The bridge consumes /planner_cmd_vel. This prevents the same
+ # command from reaching the serial driver in two formats.
+ "cmd_vel": "/planner_disabled_cmd_vel",
+ "akm_cmd_vel": "/ackermann_cmd",
+ "publish_tf": False,
+ "gyro_z_low_pass_alpha": 0.6,
+ }],
+ )
+
+ cmd_vel_bridge = Node(
+ package="planner",
+ executable="ackermann_cmd_bridge.py",
+ name="planner_ackermann_cmd_bridge",
+ output="screen",
+ condition=IfCondition(enable_motion),
+ parameters=[{
+ "input_topic": "/planner_cmd_vel",
+ "output_topic": "/ackermann_cmd",
+ "wheelbase": wheelbase_value,
+ "frame_id": "base_link",
+ "max_steering_angle": max_steering_angle_value,
+ "command_timeout": 0.25,
+ "watchdog_rate": 10.0,
+ "state_log_period": state_log_period_value,
+ }],
+ )
+
+ ekf_node = Node(
+ package="robot_localization",
+ executable="ekf_node",
+ name="ekf_filter_node",
+ output="screen",
+ parameters=[ekf_path, {"use_sim_time": use_sim_time_value}],
+ remappings=[("odometry/filtered", "/odom_combined")],
+ )
+
+ lidar_node = Node(
+ package="lslidar_driver",
+ executable="lslidar_driver_node",
+ name="lslidar_driver_node",
+ output="screen",
+ condition=IfCondition(start_lidar),
+ parameters=[lidar_params, {
+ "interface_selection": "serial",
+ "serial_port_": lidar_serial_port,
+ "lidar_name": lidar_model,
+ }],
+ )
+
+ map_server = Node(
+ package="nav2_map_server",
+ executable="map_server",
+ name="map_server",
+ output="screen",
+ parameters=[{"yaml_filename": map_yaml, "use_sim_time": use_sim_time_value}],
+ )
+
+ amcl_node = Node(
+ package="nav2_amcl",
+ executable="amcl",
+ name="amcl",
+ output="screen",
+ parameters=[amcl_path, {"use_sim_time": use_sim_time_value}],
+ )
+
+ lifecycle_manager = Node(
+ package="nav2_lifecycle_manager",
+ executable="lifecycle_manager",
+ name="lifecycle_manager_localization",
+ output="screen",
+ parameters=[{
+ "use_sim_time": use_sim_time_value,
+ "autostart": True,
+ "node_names": ["map_server", "amcl"],
+ }],
+ )
+
+ planner_node = Node(
+ package="planner",
+ executable="grid_astar_theta_node",
+ name="grid_astar_theta_planner",
+ output="screen",
+ parameters=[planner_path, {
+ "use_sim_time": use_sim_time_value,
+ "use_grid_heuristic": use_grid_heuristic_value,
+ "heuristic_weight": heuristic_weight_value,
+ "goal_yaw_heuristic_weight": goal_yaw_heuristic_weight_value,
+ "state_log_period": state_log_period_value,
+ "search_progress_log_period_ms": search_progress_log_period_ms_value,
+ }],
+ )
+
+ tracker_node = Node(
+ package="planner",
+ executable="topology_pure_pursuit_node.py",
+ name="topology_pure_pursuit",
+ output="screen",
+ parameters=[tracker_path, {
+ "use_sim_time": use_sim_time_value,
+ "publish_cmd_vel": enable_motion_value,
+ "cmd_vel_topic": "/planner_cmd_vel",
+ "state_log_period": state_log_period_value,
+ }],
+ )
+
+ return LaunchDescription([
+ DeclareLaunchArgument(
+ "map",
+ description="Required: real 5x5m map YAML used by map_server.",
+ ),
+ DeclareLaunchArgument(
+ "lidar_params",
+ description="Required: LSLIDAR YAML matching the actual sensor/interface.",
+ ),
+ DeclareLaunchArgument("lidar_serial_port", default_value="/dev/ttyCH343USB0"),
+ DeclareLaunchArgument("lidar_model", default_value="N10"),
+ DeclareLaunchArgument(
+ "start_lidar",
+ default_value="true",
+ description="Start LSLIDAR. Set false when /scan is provided externally.",
+ ),
+ DeclareLaunchArgument("serial_port", default_value="/dev/ttyACM0"),
+ DeclareLaunchArgument("serial_baud", default_value="115200"),
+ DeclareLaunchArgument(
+ "enable_motion",
+ default_value="false",
+ description="Publish motion commands. Keep false for the first planner-only test.",
+ ),
+ DeclareLaunchArgument("wheelbase", default_value="0.143"),
+ DeclareLaunchArgument("max_steering_angle", default_value="0.60"),
+ DeclareLaunchArgument(
+ "use_grid_heuristic",
+ default_value="true",
+ description="Use obstacle-aware 2D grid distance as the Hybrid A* heuristic.",
+ ),
+ DeclareLaunchArgument(
+ "heuristic_weight",
+ default_value="1.25",
+ description="Weighted A* heuristic multiplier. Higher is faster but less optimal.",
+ ),
+ DeclareLaunchArgument(
+ "goal_yaw_heuristic_weight",
+ default_value="0.0",
+ description="Weight for final yaw guidance in the Hybrid A* heuristic.",
+ ),
+ DeclareLaunchArgument(
+ "state_log_period",
+ default_value="1.0",
+ description="Seconds between runtime state logs. Set 0.0 to disable.",
+ ),
+ DeclareLaunchArgument(
+ "search_progress_log_period_ms",
+ default_value="500.0",
+ description="Milliseconds between Hybrid A* search progress logs. Set 0.0 to disable.",
+ ),
+ DeclareLaunchArgument("use_sim_time", default_value="false"),
+ robot_state_publisher,
+ base_node,
+ cmd_vel_bridge,
+ ekf_node,
+ lidar_node,
+ map_server,
+ amcl_node,
+ lifecycle_manager,
+ planner_node,
+ tracker_node,
+ ])
diff --git a/src/planner/maps/real_10x10.yaml b/src/planner/maps/real_10x10.yaml
new file mode 100644
index 0000000..fcce673
--- /dev/null
+++ b/src/planner/maps/real_10x10.yaml
@@ -0,0 +1,7 @@
+image: real_5x5.pgm
+mode: trinary
+resolution: 0.10
+origin: [-5.0, -5.0, 0.0]
+negate: 0
+occupied_thresh: 0.65
+free_thresh: 0.25
diff --git a/src/planner/maps/real_5x5.pgm b/src/planner/maps/real_5x5.pgm
new file mode 100644
index 0000000..a837739
Binary files /dev/null and b/src/planner/maps/real_5x5.pgm differ
diff --git a/src/planner/maps/real_5x5.yaml b/src/planner/maps/real_5x5.yaml
new file mode 100644
index 0000000..028c58a
--- /dev/null
+++ b/src/planner/maps/real_5x5.yaml
@@ -0,0 +1,7 @@
+image: real_5x5.pgm
+mode: trinary
+resolution: 0.05
+origin: [-2.5, -2.5, 0.0]
+negate: 0
+occupied_thresh: 0.65
+free_thresh: 0.25
diff --git a/src/planner/package.xml b/src/planner/package.xml
index ce7f983..1a204f2 100644
--- a/src/planner/package.xml
+++ b/src/planner/package.xml
@@ -1,12 +1,12 @@
-
-
-
- planner
- 0.0.0
- Minimal Nav2 global planning stack: slam_toolbox + planner_server + costmaps + lifecycle_manager
- sunrise
- TODO: License declaration
-
+
+
+
+ planner
+ 0.0.0
+ Lightweight Ackermann Hybrid A* planner, path tracker, and real-robot test tools
+ sunrise
+ TODO: License declaration
+
ament_cmake
rclcpp
@@ -16,19 +16,31 @@
slam_toolbox
nav2_planner
nav2_costmap_2d
- nav2_lifecycle_manager
nav2_common
nav2_util
tf2
tf2_geometry_msgs
tf2_ros
+ ament_index_python
+ ackermann_msgs
+ launch
+ launch_ros
+ origincar_base
+ origincar_description
+ robot_localization
+ nav2_map_server
+ nav2_amcl
+ nav2_lifecycle_manager
+ lslidar_driver
+ sensor_msgs
rclpy
+ rcl_interfaces
python3-yaml
ament_lint_auto
- ament_lint_common
-
-
- ament_cmake
-
-
+ ament_lint_common
+
+
+ ament_cmake
+
+
diff --git a/src/planner/scripts/ackermann_cmd_bridge.py b/src/planner/scripts/ackermann_cmd_bridge.py
new file mode 100755
index 0000000..ca7ba9f
--- /dev/null
+++ b/src/planner/scripts/ackermann_cmd_bridge.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""Convert Twist yaw-rate commands to Ackermann steering commands."""
+
+import math
+import time
+
+import rclpy
+from ackermann_msgs.msg import AckermannDriveStamped
+from geometry_msgs.msg import Twist
+from rclpy.node import Node
+
+
+class AckermannCmdBridge(Node):
+ def __init__(self):
+ super().__init__("planner_ackermann_cmd_bridge")
+ self.declare_parameter("input_topic", "/cmd_vel")
+ self.declare_parameter("output_topic", "/ackermann_cmd")
+ self.declare_parameter("wheelbase", 0.143)
+ self.declare_parameter("max_steering_angle", 0.60)
+ self.declare_parameter("frame_id", "base_link")
+ self.declare_parameter("command_timeout", 0.25)
+ self.declare_parameter("watchdog_rate", 10.0)
+ self.declare_parameter("state_log_period", 1.0)
+
+ input_topic = str(self.get_parameter("input_topic").value)
+ output_topic = str(self.get_parameter("output_topic").value)
+ self.wheelbase = float(self.get_parameter("wheelbase").value)
+ self.max_steering_angle = abs(
+ float(self.get_parameter("max_steering_angle").value)
+ )
+ self.frame_id = str(self.get_parameter("frame_id").value)
+ self.command_timeout = max(
+ 0.05, float(self.get_parameter("command_timeout").value)
+ )
+ self.state_log_period = max(
+ 0.0, float(self.get_parameter("state_log_period").value)
+ )
+ if self.wheelbase <= 0.0:
+ raise ValueError("wheelbase must be positive")
+ if self.max_steering_angle <= 0.0:
+ raise ValueError("max_steering_angle must be positive")
+
+ self.publisher = self.create_publisher(
+ AckermannDriveStamped, output_topic, 10
+ )
+ self.subscription = self.create_subscription(
+ Twist, input_topic, self._cmd_callback, 10
+ )
+ self.last_command_time = None
+ self.last_idle_publish_time = 0.0
+ self.last_state_log_time = 0.0
+ self.last_input_linear = 0.0
+ self.last_input_angular = 0.0
+ self.last_output_velocity = 0.0
+ self.last_output_steering = 0.0
+ self.last_steering_saturated = False
+ watchdog_rate = max(2.0, float(self.get_parameter("watchdog_rate").value))
+ self.watchdog_timer = self.create_timer(1.0 / watchdog_rate, self._watchdog)
+ self.get_logger().info(
+ f"bridge {input_topic} -> {output_topic}, "
+ f"wheelbase={self.wheelbase:.3f} m, "
+ f"steering_limit={self.max_steering_angle:.3f} rad, "
+ f"command_timeout={self.command_timeout:.3f} s"
+ )
+
+ def _cmd_callback(self, command):
+ self.last_command_time = time.monotonic()
+ velocity = command.linear.x
+ self.last_input_linear = velocity
+ self.last_input_angular = command.angular.z
+ if abs(velocity) <= 1e-6 or abs(command.angular.z) <= 1e-6:
+ steering = 0.0
+ raw_steering = 0.0
+ else:
+ raw_steering = math.atan(self.wheelbase * command.angular.z / velocity)
+ steering = raw_steering
+ steering = max(
+ -self.max_steering_angle,
+ min(self.max_steering_angle, steering),
+ )
+ self.last_steering_saturated = abs(raw_steering - steering) > 1e-6
+
+ self._publish(velocity, steering)
+
+ def _watchdog(self):
+ now = time.monotonic()
+ command_stale = (
+ self.last_command_time is None
+ or now - self.last_command_time > self.command_timeout
+ )
+ if command_stale and now - self.last_idle_publish_time >= 0.5:
+ self._publish(0.0, 0.0)
+ self.last_idle_publish_time = now
+
+ def _publish(self, velocity, steering):
+ self.last_output_velocity = float(velocity)
+ self.last_output_steering = float(steering)
+ output = AckermannDriveStamped()
+ output.header.stamp = self.get_clock().now().to_msg()
+ output.header.frame_id = self.frame_id
+ output.drive.speed = self.last_output_velocity
+ output.drive.steering_angle = self.last_output_steering
+ self.publisher.publish(output)
+ self._maybe_log_state()
+
+ def _maybe_log_state(self):
+ if self.state_log_period <= 0.0:
+ return
+ now = time.monotonic()
+ if now - self.last_state_log_time < self.state_log_period:
+ return
+ self.last_state_log_time = now
+ if self.last_command_time is None:
+ command_age = None
+ stale = True
+ else:
+ command_age = max(0.0, now - self.last_command_time)
+ stale = command_age > self.command_timeout
+ age_text = "none" if command_age is None else f"{command_age:.3f}"
+ self.get_logger().info(
+ "ackermann_state "
+ f"input_twist=({self.last_input_linear:.3f},{self.last_input_angular:.3f}) "
+ f"output=(speed={self.last_output_velocity:.3f},"
+ f"steering={self.last_output_steering:.3f}) "
+ f"command_age={age_text} stale={stale} "
+ f"steering_saturated={self.last_steering_saturated}"
+ )
+
+ def stop(self):
+ for _ in range(3):
+ self._publish(0.0, 0.0)
+
+
+def main():
+ rclpy.init()
+ node = AckermannCmdBridge()
+ try:
+ rclpy.spin(node)
+ except KeyboardInterrupt:
+ pass
+ finally:
+ node.stop()
+ node.destroy_node()
+ rclpy.shutdown()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/planner/scripts/evaluate_navigation_test.py b/src/planner/scripts/evaluate_navigation_test.py
new file mode 100755
index 0000000..ffd959b
--- /dev/null
+++ b/src/planner/scripts/evaluate_navigation_test.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+"""Evaluate navigation test summaries against explicit acceptance thresholds."""
+
+import argparse
+import glob
+import json
+from pathlib import Path
+import sys
+
+
+def nested_value(data, *keys):
+ value = data
+ for key in keys:
+ if not isinstance(value, dict) or key not in value:
+ return None
+ value = value[key]
+ return value
+
+
+def check_max(failures, label, value, limit):
+ if value is None:
+ failures.append(f"{label}: missing")
+ elif value > limit:
+ failures.append(f"{label}: {value:.4f} > {limit:.4f}")
+
+
+def check_min(failures, label, value, limit):
+ if value is None:
+ failures.append(f"{label}: missing")
+ elif value < limit:
+ failures.append(f"{label}: {value:.4f} < {limit:.4f}")
+
+
+def evaluate_planning(data, args):
+ failures = []
+ ok_count = int(data.get("plan_ok_status_count", 0))
+ fail_count = int(data.get("plan_fail_status_count", 0))
+ attempts = ok_count + fail_count
+ success_rate = ok_count / attempts if attempts else None
+ check_min(failures, "planning success rate", success_rate, args.min_plan_success)
+ check_max(
+ failures,
+ "planning latency p95 (s)",
+ nested_value(data, "planning_latency_sec", "p95"),
+ args.max_planning_p95,
+ )
+ check_max(
+ failures,
+ "occupied path points",
+ data.get("path_occupied_points_max"),
+ args.max_occupied_points,
+ )
+ minimum_radius = data.get("minimum_planned_turning_radius_m")
+ if minimum_radius is not None:
+ check_min(
+ failures,
+ "minimum turning radius (m)",
+ minimum_radius,
+ args.min_turning_radius,
+ )
+ elif int(data.get("plan_count", 0)) <= 0:
+ failures.append("minimum turning radius (m): missing because no path was recorded")
+ return failures
+
+
+def evaluate_tracking(data, args):
+ failures = []
+ check_max(
+ failures,
+ "cross-track RMS (m)",
+ nested_value(data, "cross_track_error_m", "rms"),
+ args.max_cross_track_rms,
+ )
+ check_max(
+ failures,
+ "cross-track p95 (m)",
+ nested_value(data, "cross_track_error_m", "p95"),
+ args.max_cross_track_p95,
+ )
+ check_max(
+ failures,
+ "cross-track max (m)",
+ nested_value(data, "cross_track_error_m", "max"),
+ args.max_cross_track,
+ )
+ check_max(
+ failures,
+ "final goal error (m)",
+ data.get("final_goal_error_m"),
+ args.max_goal_error,
+ )
+ check_max(
+ failures,
+ "cancel-to-zero p95 (s)",
+ nested_value(data, "cancel_to_zero_command_sec", "p95"),
+ args.max_cancel_latency,
+ )
+ check_max(
+ failures,
+ "Ackermann drive speed max (m/s)",
+ data.get("max_abs_drive_speed"),
+ args.max_drive_speed,
+ )
+ check_max(
+ failures,
+ "Ackermann steering max (rad)",
+ data.get("max_abs_steering_angle"),
+ args.max_steering_angle,
+ )
+ transform_failures = int(data.get("transform_failures", 0))
+ if transform_failures > args.max_transform_failures:
+ failures.append(
+ f"transform failures: {transform_failures} > {args.max_transform_failures}"
+ )
+ return failures
+
+
+def resolve_summary_paths(values):
+ paths = []
+ unmatched = []
+ for value in values:
+ expanded_value = str(Path(value).expanduser())
+ matches = sorted(glob.glob(expanded_value))
+ if not matches:
+ unmatched.append(value)
+ continue
+ paths.extend(Path(match) for match in matches if Path(match).is_file())
+ return paths, unmatched
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("mode", choices=("planning", "tracking"))
+ parser.add_argument("summaries", nargs="+")
+ parser.add_argument("--min-plan-success", type=float, default=0.95)
+ parser.add_argument("--max-planning-p95", type=float, default=0.50)
+ parser.add_argument("--max-occupied-points", type=int, default=0)
+ parser.add_argument("--min-turning-radius", type=float, default=0.36)
+ parser.add_argument("--max-cross-track-rms", type=float, default=0.12)
+ parser.add_argument("--max-cross-track-p95", type=float, default=0.20)
+ parser.add_argument("--max-cross-track", type=float, default=0.30)
+ parser.add_argument("--max-goal-error", type=float, default=0.15)
+ parser.add_argument("--max-cancel-latency", type=float, default=0.25)
+ parser.add_argument("--max-drive-speed", type=float, default=0.21)
+ parser.add_argument("--max-steering-angle", type=float, default=0.601)
+ parser.add_argument("--max-transform-failures", type=int, default=0)
+ args = parser.parse_args()
+
+ summary_paths, unmatched = resolve_summary_paths(args.summaries)
+ if unmatched:
+ for pattern in unmatched:
+ print(f"ERROR no summary files matched: {pattern}", file=sys.stderr)
+ if not summary_paths:
+ return 2
+
+ overall_success = True
+ for summary_path in summary_paths:
+ try:
+ data = json.loads(summary_path.read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError) as exc:
+ overall_success = False
+ print(f"FAIL {summary_path}")
+ print(f" - unable to read valid JSON: {exc}")
+ continue
+ if args.mode == "planning":
+ failures = evaluate_planning(data, args)
+ else:
+ failures = evaluate_tracking(data, args)
+ if failures:
+ overall_success = False
+ print(f"FAIL {summary_path}")
+ for failure in failures:
+ print(f" - {failure}")
+ else:
+ print(f"PASS {summary_path}")
+ return 0 if overall_success and not unmatched else 1
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/src/planner/scripts/goal_reach_debug_collector.py b/src/planner/scripts/goal_reach_debug_collector.py
new file mode 100755
index 0000000..fd760a4
--- /dev/null
+++ b/src/planner/scripts/goal_reach_debug_collector.py
@@ -0,0 +1,717 @@
+#!/usr/bin/env python3
+"""Collect data needed to diagnose why the robot does not reach a goal.
+
+This node is read-only. It subscribes to planner/tracker topics, queries key
+parameters, samples odom/command state, and writes one JSON report.
+"""
+
+import json
+import math
+from pathlib import Path
+import re
+import time
+
+import rclpy
+from geometry_msgs.msg import PoseStamped, Twist
+from nav_msgs.msg import Odometry, Path as NavPath
+from rcl_interfaces.msg import ParameterType
+from rcl_interfaces.srv import GetParameters
+from rclpy.duration import Duration
+from rclpy.node import Node
+from rclpy.qos import DurabilityPolicy, QoSProfile, ReliabilityPolicy
+from rclpy.time import Time
+from std_msgs.msg import String
+from tf2_ros import Buffer, TransformException, TransformListener
+
+try:
+ from ackermann_msgs.msg import AckermannDriveStamped
+except ImportError: # pragma: no cover - ackermann_msgs is a runtime dependency.
+ AckermannDriveStamped = None
+
+
+PLAN_OK_RE = re.compile(
+ r"plan_ok.*points=(?P\d+).*iterations=(?P\d+)"
+ r".*planning_ms=(?P[-+]?\d+(?:\.\d+)?)"
+ r".*goal_error=(?P[-+]?\d+(?:\.\d+)?)"
+ r".*yaw_error=(?P[-+]?\d+(?:\.\d+)?)"
+)
+
+
+def yaw_from_quaternion(q):
+ return math.atan2(
+ 2.0 * (q.w * q.z + q.x * q.y),
+ 1.0 - 2.0 * (q.y * q.y + q.z * q.z),
+ )
+
+
+def normalize_angle(angle):
+ return math.atan2(math.sin(angle), math.cos(angle))
+
+
+def finite_or_none(value):
+ if value is None:
+ return None
+ if isinstance(value, float) and not math.isfinite(value):
+ return None
+ return value
+
+
+def endpoint_to_dict(endpoint):
+ return {
+ "node_name": getattr(endpoint, "node_name", ""),
+ "node_namespace": getattr(endpoint, "node_namespace", ""),
+ "topic_type": getattr(endpoint, "topic_type", ""),
+ }
+
+
+def parameter_value_to_python_compatible(value):
+ """Convert rcl_interfaces/msg/ParameterValue on ROS 2 Humble and later."""
+ if value.type == ParameterType.PARAMETER_BOOL:
+ return bool(value.bool_value)
+ if value.type == ParameterType.PARAMETER_INTEGER:
+ return int(value.integer_value)
+ if value.type == ParameterType.PARAMETER_DOUBLE:
+ return float(value.double_value)
+ if value.type == ParameterType.PARAMETER_STRING:
+ return str(value.string_value)
+ if value.type == ParameterType.PARAMETER_BYTE_ARRAY:
+ return list(value.byte_array_value)
+ if value.type == ParameterType.PARAMETER_BOOL_ARRAY:
+ return list(value.bool_array_value)
+ if value.type == ParameterType.PARAMETER_INTEGER_ARRAY:
+ return list(value.integer_array_value)
+ if value.type == ParameterType.PARAMETER_DOUBLE_ARRAY:
+ return list(value.double_array_value)
+ if value.type == ParameterType.PARAMETER_STRING_ARRAY:
+ return list(value.string_array_value)
+ return None
+
+
+class GoalReachDebugCollector(Node):
+ def __init__(self):
+ super().__init__("goal_reach_debug_collector")
+
+ self.declare_parameter("duration", 30.0)
+ self.declare_parameter("sample_rate", 10.0)
+ self.declare_parameter("output_dir", "~/yiliao_ws/test_results")
+ self.declare_parameter("test_name", "goal_reach_debug")
+ self.declare_parameter("map_frame", "map")
+ self.declare_parameter("odom_topic", "/odom")
+ self.declare_parameter("goal_pose_topic", "/goal_pose")
+ self.declare_parameter("plan_topic", "/plan")
+ self.declare_parameter("cmd_vel_topic", "/planner_cmd_vel")
+ self.declare_parameter("ackermann_cmd_topic", "/ackermann_cmd")
+ self.declare_parameter("planner_status_topic", "/hybrid_astar_status")
+ self.declare_parameter("tracker_status_topic", "/pure_pursuit_status")
+ self.declare_parameter("planner_node", "/grid_astar_theta_planner")
+ self.declare_parameter("tracker_node", "/topology_pure_pursuit")
+ self.declare_parameter("tf_timeout", 0.10)
+ self.declare_parameter("max_odom_age", 2.0)
+ self.declare_parameter("include_plan_points", True)
+
+ self.duration = max(1.0, float(self.get_parameter("duration").value))
+ self.sample_rate = max(1.0, float(self.get_parameter("sample_rate").value))
+ self.map_frame = str(self.get_parameter("map_frame").value)
+ self.odom_topic = str(self.get_parameter("odom_topic").value)
+ self.goal_pose_topic = str(self.get_parameter("goal_pose_topic").value)
+ self.plan_topic = str(self.get_parameter("plan_topic").value)
+ self.cmd_vel_topic = str(self.get_parameter("cmd_vel_topic").value)
+ self.ackermann_cmd_topic = str(self.get_parameter("ackermann_cmd_topic").value)
+ self.planner_status_topic = str(
+ self.get_parameter("planner_status_topic").value
+ )
+ self.tracker_status_topic = str(
+ self.get_parameter("tracker_status_topic").value
+ )
+ self.planner_node = str(self.get_parameter("planner_node").value)
+ self.tracker_node = str(self.get_parameter("tracker_node").value)
+ self.tf_timeout = float(self.get_parameter("tf_timeout").value)
+ self.max_odom_age = float(self.get_parameter("max_odom_age").value)
+ self.include_plan_points = bool(
+ self.get_parameter("include_plan_points").value
+ )
+
+ output_dir = Path(str(self.get_parameter("output_dir").value)).expanduser()
+ output_dir.mkdir(parents=True, exist_ok=True)
+ timestamp = time.strftime("%Y%m%d_%H%M%S")
+ test_name = str(self.get_parameter("test_name").value)
+ self.output_path = output_dir / f"{test_name}_{timestamp}.json"
+
+ self.started_wall = time.time()
+ self.started_mono = time.monotonic()
+ self.finished = False
+
+ self.latest_odom = None
+ self.latest_goal = None
+ self.latest_plan_points = []
+ self.latest_cmd = None
+ self.latest_ackermann = None
+ self.latest_planner_status = ""
+ self.latest_tracker_status = ""
+ self.last_plan_ok = None
+ self.remote_parameters = {}
+
+ self.samples = []
+ self.planner_status_events = []
+ self.tracker_status_events = []
+ self.plan_events = []
+ self.goal_events = []
+
+ self.min_distance_to_goal = None
+ self.min_distance_to_plan_end = None
+ self.max_abs_cmd_linear = 0.0
+ self.max_abs_cmd_angular = 0.0
+ self.max_abs_ackermann_speed = 0.0
+ self.max_abs_steering_angle = 0.0
+ self.tf_failures = 0
+
+ self.tf_buffer = Buffer()
+ self.tf_listener = TransformListener(self.tf_buffer, self)
+
+ goal_qos = QoSProfile(
+ depth=10,
+ reliability=ReliabilityPolicy.RELIABLE,
+ durability=DurabilityPolicy.TRANSIENT_LOCAL,
+ )
+ self.subscriptions_keepalive = [
+ self.create_subscription(Odometry, self.odom_topic, self._odom_cb, 50),
+ self.create_subscription(
+ PoseStamped, self.goal_pose_topic, self._goal_cb, goal_qos
+ ),
+ self.create_subscription(NavPath, self.plan_topic, self._plan_cb, 10),
+ self.create_subscription(Twist, self.cmd_vel_topic, self._cmd_cb, 50),
+ self.create_subscription(
+ String, self.planner_status_topic, self._planner_status_cb, 50
+ ),
+ self.create_subscription(
+ String, self.tracker_status_topic, self._tracker_status_cb, 50
+ ),
+ ]
+ if AckermannDriveStamped is not None:
+ self.subscriptions_keepalive.append(
+ self.create_subscription(
+ AckermannDriveStamped,
+ self.ackermann_cmd_topic,
+ self._ackermann_cb,
+ 50,
+ )
+ )
+
+ self.sample_timer = self.create_timer(1.0 / self.sample_rate, self._sample)
+ self.done_timer = self.create_timer(0.20, self._maybe_finish)
+ self.get_logger().info(
+ f"collecting goal reach debug data for {self.duration:.1f}s -> {self.output_path}"
+ )
+
+ def elapsed(self):
+ return max(0.0, time.monotonic() - self.started_mono)
+
+ def _odom_cb(self, msg):
+ self.latest_odom = msg
+
+ def _goal_cb(self, msg):
+ goal = {
+ "elapsed_sec": self.elapsed(),
+ "frame_id": msg.header.frame_id or self.map_frame,
+ "x": msg.pose.position.x,
+ "y": msg.pose.position.y,
+ "yaw": yaw_from_quaternion(msg.pose.orientation),
+ }
+ self.latest_goal = goal
+ self.goal_events.append(goal)
+
+ def _plan_cb(self, msg):
+ points = [
+ {
+ "x": pose.pose.position.x,
+ "y": pose.pose.position.y,
+ "yaw": yaw_from_quaternion(pose.pose.orientation),
+ }
+ for pose in msg.poses
+ ]
+ self.latest_plan_points = points
+ summary = self._plan_summary(points, msg.header.frame_id or self.map_frame)
+ summary["elapsed_sec"] = self.elapsed()
+ self.plan_events.append(summary)
+
+ def _cmd_cb(self, msg):
+ self.latest_cmd = {
+ "linear_x": msg.linear.x,
+ "angular_z": msg.angular.z,
+ }
+ self.max_abs_cmd_linear = max(self.max_abs_cmd_linear, abs(msg.linear.x))
+ self.max_abs_cmd_angular = max(self.max_abs_cmd_angular, abs(msg.angular.z))
+
+ def _ackermann_cb(self, msg):
+ self.latest_ackermann = {
+ "speed": msg.drive.speed,
+ "steering_angle": msg.drive.steering_angle,
+ }
+ self.max_abs_ackermann_speed = max(
+ self.max_abs_ackermann_speed, abs(msg.drive.speed)
+ )
+ self.max_abs_steering_angle = max(
+ self.max_abs_steering_angle, abs(msg.drive.steering_angle)
+ )
+
+ def _planner_status_cb(self, msg):
+ self.latest_planner_status = msg.data
+ event = {"elapsed_sec": self.elapsed(), "data": msg.data}
+ parsed = self._parse_plan_ok(msg.data)
+ if parsed is not None:
+ event["parsed_plan_ok"] = parsed
+ self.last_plan_ok = parsed
+ self.planner_status_events.append(event)
+
+ def _tracker_status_cb(self, msg):
+ self.latest_tracker_status = msg.data
+ self.tracker_status_events.append(
+ {"elapsed_sec": self.elapsed(), "data": msg.data}
+ )
+
+ def _parse_plan_ok(self, text):
+ match = PLAN_OK_RE.search(text)
+ if not match:
+ return None
+ parsed = {}
+ for key, value in match.groupdict().items():
+ parsed[key] = int(value) if key in {"points", "iterations"} else float(value)
+ return parsed
+
+ def _sample(self):
+ pose = self._current_pose_map()
+ pose_available = self._pose_available(pose)
+ goal = self.latest_goal
+ plan_end = self.latest_plan_points[-1] if self.latest_plan_points else None
+
+ distance_to_goal = None
+ yaw_error_to_goal = None
+ if pose_available and goal is not None:
+ distance_to_goal = math.hypot(goal["x"] - pose["x"], goal["y"] - pose["y"])
+ yaw_error_to_goal = abs(normalize_angle(goal["yaw"] - pose["yaw"]))
+ self.min_distance_to_goal = self._min_or_value(
+ self.min_distance_to_goal, distance_to_goal
+ )
+
+ distance_to_plan_end = None
+ yaw_error_to_plan_end = None
+ if pose_available and plan_end is not None:
+ distance_to_plan_end = math.hypot(
+ plan_end["x"] - pose["x"], plan_end["y"] - pose["y"]
+ )
+ yaw_error_to_plan_end = abs(normalize_angle(plan_end["yaw"] - pose["yaw"]))
+ self.min_distance_to_plan_end = self._min_or_value(
+ self.min_distance_to_plan_end, distance_to_plan_end
+ )
+
+ plan_end_to_goal = None
+ plan_end_yaw_to_goal = None
+ if plan_end is not None and goal is not None:
+ plan_end_to_goal = math.hypot(plan_end["x"] - goal["x"], plan_end["y"] - goal["y"])
+ plan_end_yaw_to_goal = abs(normalize_angle(plan_end["yaw"] - goal["yaw"]))
+
+ self.samples.append({
+ "elapsed_sec": self.elapsed(),
+ "pose_map": pose,
+ "goal": goal,
+ "plan_end": plan_end,
+ "distance_to_goal": finite_or_none(distance_to_goal),
+ "yaw_error_to_goal": finite_or_none(yaw_error_to_goal),
+ "distance_to_plan_end": finite_or_none(distance_to_plan_end),
+ "yaw_error_to_plan_end": finite_or_none(yaw_error_to_plan_end),
+ "plan_end_to_goal": finite_or_none(plan_end_to_goal),
+ "plan_end_yaw_to_goal": finite_or_none(plan_end_yaw_to_goal),
+ "cmd_vel": self.latest_cmd,
+ "ackermann_cmd": self.latest_ackermann,
+ "planner_status": self.latest_planner_status,
+ "tracker_status": self.latest_tracker_status,
+ })
+
+ def _min_or_value(self, old, new):
+ if new is None:
+ return old
+ if old is None:
+ return new
+ return min(old, new)
+
+ def _pose_available(self, pose):
+ return (
+ isinstance(pose, dict)
+ and pose.get("available") is True
+ and "x" in pose
+ and "y" in pose
+ and "yaw" in pose
+ )
+
+ def _current_pose_map(self):
+ if self.latest_odom is None:
+ return None
+
+ msg = self.latest_odom
+ stamp = Time.from_msg(msg.header.stamp)
+ if msg.header.stamp.sec == 0 and msg.header.stamp.nanosec == 0:
+ age = 0.0
+ else:
+ age = (self.get_clock().now() - stamp).nanoseconds * 1e-9
+ if age < 0.0:
+ age = 0.0
+ if age > self.max_odom_age:
+ return {
+ "available": False,
+ "reason": "odom_too_old",
+ "age_sec": age,
+ }
+
+ source_frame = msg.header.frame_id or self.map_frame
+ x = msg.pose.pose.position.x
+ y = msg.pose.pose.position.y
+ yaw = yaw_from_quaternion(msg.pose.pose.orientation)
+ transformed = self._transform_pose_2d(x, y, yaw, source_frame)
+ if transformed is None:
+ return {
+ "available": False,
+ "reason": "tf_unavailable",
+ "source_frame": source_frame,
+ }
+ x, y, yaw = transformed
+ return {
+ "available": True,
+ "frame_id": self.map_frame,
+ "source_frame": source_frame,
+ "x": x,
+ "y": y,
+ "yaw": yaw,
+ "odom_age_sec": age,
+ "odom_linear_x": msg.twist.twist.linear.x,
+ "odom_angular_z": msg.twist.twist.angular.z,
+ }
+
+ def _transform_pose_2d(self, x, y, yaw, source_frame):
+ if source_frame == self.map_frame:
+ return x, y, yaw
+ try:
+ transform = self.tf_buffer.lookup_transform(
+ self.map_frame,
+ source_frame,
+ Time(),
+ timeout=Duration(seconds=self.tf_timeout),
+ )
+ except TransformException:
+ self.tf_failures += 1
+ return None
+
+ t = transform.transform.translation
+ transform_yaw = yaw_from_quaternion(transform.transform.rotation)
+ cos_yaw = math.cos(transform_yaw)
+ sin_yaw = math.sin(transform_yaw)
+ target_x = t.x + cos_yaw * x - sin_yaw * y
+ target_y = t.y + sin_yaw * x + cos_yaw * y
+ target_yaw = normalize_angle(transform_yaw + yaw)
+ return target_x, target_y, target_yaw
+
+ def _plan_summary(self, points, frame_id):
+ if not points:
+ return {
+ "frame_id": frame_id,
+ "points": 0,
+ "length": 0.0,
+ "start": None,
+ "end": None,
+ }
+ length = 0.0
+ reverse_segments = 0
+ for a, b in zip(points, points[1:]):
+ length += math.hypot(b["x"] - a["x"], b["y"] - a["y"])
+ heading = math.atan2(b["y"] - a["y"], b["x"] - a["x"])
+ if math.cos(normalize_angle(a["yaw"] - heading)) < 0.0:
+ reverse_segments += 1
+ return {
+ "frame_id": frame_id,
+ "points": len(points),
+ "length": length,
+ "start": points[0],
+ "end": points[-1],
+ "reverse_segment_fraction": (
+ reverse_segments / max(1, len(points) - 1)
+ ),
+ }
+
+ def _maybe_finish(self):
+ if self.elapsed() >= self.duration:
+ self.finished = True
+
+ def collect_remote_parameters(self):
+ parameter_targets = {
+ "planner": (
+ self.planner_node,
+ [
+ "goal_xy_tolerance",
+ "goal_yaw_tolerance",
+ "require_goal_yaw",
+ "resolution",
+ "yaw_bins",
+ "primitive_length",
+ "min_turning_radius",
+ "heuristic_weight",
+ "goal_yaw_heuristic_weight",
+ "planning_timeout_ms",
+ "max_iterations",
+ ],
+ ),
+ "tracker": (
+ self.tracker_node,
+ [
+ "goal_tolerance",
+ "goal_yaw_tolerance",
+ "align_goal_yaw",
+ "publish_cmd_vel",
+ "cmd_vel_topic",
+ "lookahead_distance",
+ "linear_speed",
+ "min_linear_speed",
+ "reverse_speed",
+ "slowdown_distance",
+ "curvature_slowdown_gain",
+ "min_turning_radius",
+ "max_angular_speed",
+ ],
+ ),
+ }
+ for label, (node_name, names) in parameter_targets.items():
+ normalized_node_name = node_name if node_name.startswith("/") else f"/{node_name}"
+ service_name = f"{normalized_node_name}/get_parameters"
+ client = self.create_client(GetParameters, service_name)
+ if not client.wait_for_service(timeout_sec=1.0):
+ self.remote_parameters[label] = {
+ "node": node_name,
+ "service": service_name,
+ "error": "parameter_service_unavailable",
+ }
+ continue
+ request = GetParameters.Request()
+ request.names = list(names)
+ future = client.call_async(request)
+ rclpy.spin_until_future_complete(self, future, timeout_sec=2.0)
+ if not future.done() or future.result() is None:
+ self.remote_parameters[label] = {
+ "node": node_name,
+ "service": service_name,
+ "error": "parameter_query_timeout",
+ }
+ continue
+ response = future.result()
+ self.remote_parameters[label] = {
+ "node": node_name,
+ "service": service_name,
+ "values": {
+ name: parameter_value_to_python_compatible(value)
+ for name, value in zip(names, response.values)
+ },
+ }
+
+ def write_report(self):
+ report = {
+ "metadata": {
+ "started_wall_time": self.started_wall,
+ "duration_sec": self.elapsed(),
+ "output_path": str(self.output_path),
+ },
+ "topics": {
+ "odom": self.odom_topic,
+ "goal_pose": self.goal_pose_topic,
+ "plan": self.plan_topic,
+ "cmd_vel": self.cmd_vel_topic,
+ "ackermann_cmd": self.ackermann_cmd_topic,
+ "planner_status": self.planner_status_topic,
+ "tracker_status": self.tracker_status_topic,
+ },
+ "topic_endpoints": self._topic_endpoints_report(),
+ "remote_parameters": self.remote_parameters,
+ "latest": {
+ "goal": self.latest_goal,
+ "plan": self._plan_summary(self.latest_plan_points, self.map_frame),
+ "pose_map": self._current_pose_map(),
+ "cmd_vel": self.latest_cmd,
+ "ackermann_cmd": self.latest_ackermann,
+ "planner_status": self.latest_planner_status,
+ "tracker_status": self.latest_tracker_status,
+ "last_plan_ok": self.last_plan_ok,
+ },
+ "metrics": self._metrics(),
+ "diagnostic_hints": self._diagnostic_hints(),
+ "events": {
+ "goals": self.goal_events,
+ "plans": self.plan_events,
+ "planner_status": self.planner_status_events,
+ "tracker_status": self.tracker_status_events,
+ },
+ "samples": self.samples,
+ }
+ if self.include_plan_points:
+ report["latest"]["plan_points"] = self.latest_plan_points
+
+ self.output_path.write_text(
+ json.dumps(report, indent=2, ensure_ascii=False),
+ encoding="utf-8",
+ )
+ self.get_logger().info(f"wrote goal reach debug report: {self.output_path}")
+ for hint in report["diagnostic_hints"]:
+ self.get_logger().warn(hint)
+
+ def _topic_endpoints_report(self):
+ topics = [
+ self.odom_topic,
+ self.goal_pose_topic,
+ self.plan_topic,
+ self.cmd_vel_topic,
+ self.ackermann_cmd_topic,
+ self.planner_status_topic,
+ self.tracker_status_topic,
+ ]
+ report = {}
+ for topic in topics:
+ publishers = self.get_publishers_info_by_topic(topic)
+ subscribers = self.get_subscriptions_info_by_topic(topic)
+ report[topic] = {
+ "publisher_count": len(publishers),
+ "subscriber_count": len(subscribers),
+ "publishers": [endpoint_to_dict(endpoint) for endpoint in publishers],
+ "subscribers": [endpoint_to_dict(endpoint) for endpoint in subscribers],
+ }
+ return report
+
+ def _metrics(self):
+ final = self.samples[-1] if self.samples else {}
+ planner_texts = [event["data"] for event in self.planner_status_events]
+ tracker_texts = [event["data"] for event in self.tracker_status_events]
+ return {
+ "sample_count": len(self.samples),
+ "goal_count": len(self.goal_events),
+ "plan_count": len(self.plan_events),
+ "planner_status_count": len(self.planner_status_events),
+ "tracker_status_count": len(self.tracker_status_events),
+ "min_distance_to_goal": finite_or_none(self.min_distance_to_goal),
+ "min_distance_to_plan_end": finite_or_none(self.min_distance_to_plan_end),
+ "final_distance_to_goal": final.get("distance_to_goal"),
+ "final_distance_to_plan_end": final.get("distance_to_plan_end"),
+ "final_plan_end_to_goal": final.get("plan_end_to_goal"),
+ "final_plan_end_yaw_to_goal": final.get("plan_end_yaw_to_goal"),
+ "max_abs_cmd_linear": self.max_abs_cmd_linear,
+ "max_abs_cmd_angular": self.max_abs_cmd_angular,
+ "max_abs_ackermann_speed": self.max_abs_ackermann_speed,
+ "max_abs_steering_angle": self.max_abs_steering_angle,
+ "tf_failures": self.tf_failures,
+ "saw_plan_ok": any("plan_ok" in text for text in planner_texts),
+ "saw_plan_failed": any("plan_failed" in text for text in planner_texts),
+ "saw_goal_reached": any("goal_reached" in text for text in tracker_texts),
+ "saw_align_goal_yaw": any("aligning_goal_yaw" in text for text in tracker_texts),
+ "saw_no_recent_odom": any("no_recent_odom" in text for text in tracker_texts + planner_texts),
+ }
+
+ def _diagnostic_hints(self):
+ hints = []
+ params = self.remote_parameters
+ planner = params.get("planner", {}).get("values", {})
+ tracker = params.get("tracker", {}).get("values", {})
+ metrics = self._metrics()
+
+ if not self.latest_goal:
+ hints.append("没有采集到 /goal_pose；请先运行采集脚本，再发送目标。")
+ if not self.latest_plan_points:
+ hints.append("没有采集到 /plan；planner 可能未规划成功或采集脚本启动太晚。")
+ if self.latest_odom is None:
+ hints.append(f"没有采集到 {self.odom_topic}；tracker 无法闭环到点。")
+ if metrics["tf_failures"] > 0:
+ hints.append(
+ f"TF 查询失败 {metrics['tf_failures']} 次；需要检查 map->odom 和 odom frame。"
+ )
+
+ planner_xy = planner.get("goal_xy_tolerance")
+ tracker_xy = tracker.get("goal_tolerance")
+ if isinstance(planner_xy, (int, float)) and isinstance(tracker_xy, (int, float)):
+ if planner_xy > tracker_xy:
+ hints.append(
+ "planner 的 goal_xy_tolerance 大于 tracker 的 goal_tolerance；"
+ "planner 可能发布一个离原始目标较远的路径终点。"
+ )
+
+ planner_yaw = planner.get("goal_yaw_tolerance")
+ tracker_yaw = tracker.get("goal_yaw_tolerance")
+ planner_requires_yaw = bool(planner.get("require_goal_yaw"))
+ tracker_aligns_yaw = bool(tracker.get("align_goal_yaw"))
+ if (
+ planner_requires_yaw
+ and tracker_aligns_yaw
+ and isinstance(planner_yaw, (int, float))
+ and isinstance(tracker_yaw, (int, float))
+ ):
+ if planner_yaw > tracker_yaw:
+ hints.append(
+ "planner 的 goal_yaw_tolerance 大于 tracker 的 goal_yaw_tolerance；"
+ "planner 认为合格的终点 yaw，tracker 可能继续调整。"
+ )
+
+ min_speed = tracker.get("min_linear_speed")
+ if isinstance(min_speed, (int, float)) and isinstance(tracker_xy, (int, float)):
+ if min_speed > tracker_xy:
+ hints.append(
+ "tracker 的 min_linear_speed 数值大于 goal_tolerance；"
+ "接近终点时可能仍以较高速度越过目标。"
+ )
+
+ plan_goal_error = metrics.get("final_plan_end_to_goal")
+ if isinstance(plan_goal_error, (int, float)) and isinstance(tracker_xy, (int, float)):
+ if plan_goal_error > tracker_xy:
+ hints.append(
+ "最新 /plan 的终点到 /goal_pose 的距离大于 tracker 到点半径；"
+ "tracker 跟随 /plan 时不会到达你原始输入的目标点。"
+ )
+
+ if metrics["saw_align_goal_yaw"] and not metrics["saw_goal_reached"]:
+ hints.append(
+ "采集期间出现 aligning_goal_yaw 但没有 goal_reached；"
+ "终点 yaw 对齐可能把车辆带离目标圆。"
+ )
+
+ if (
+ not metrics["saw_goal_reached"]
+ and metrics["max_abs_cmd_linear"] <= 1e-6
+ and metrics["max_abs_cmd_angular"] <= 1e-6
+ ):
+ hints.append(
+ "采集期间没有观察到速度命令；如果正在做实车到点测试，"
+ "请确认 launch 使用 enable_motion:=true，且采集的是 tracker 实际输出话题。"
+ )
+
+ ack_info = self._topic_endpoints_report().get(self.ackermann_cmd_topic, {})
+ if ack_info.get("publisher_count", 0) == 0:
+ hints.append(
+ f"{self.ackermann_cmd_topic} 没有发布者；阿克曼桥可能未启动，"
+ "通常是 enable_motion:=false。"
+ )
+
+ if not hints:
+ hints.append("未发现明显配置矛盾；请把 JSON 报告发给我继续分析。")
+ return hints
+
+
+def main(args=None):
+ rclpy.init(args=args)
+ node = GoalReachDebugCollector()
+ try:
+ node.collect_remote_parameters()
+ while rclpy.ok() and not node.finished:
+ rclpy.spin_once(node, timeout_sec=0.10)
+ node.write_report()
+ except KeyboardInterrupt:
+ node.get_logger().warn("interrupted; writing partial report")
+ node.write_report()
+ finally:
+ node.destroy_node()
+ if rclpy.ok():
+ rclpy.shutdown()
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/src/planner/scripts/grid_astar_theta_node.py b/src/planner/scripts/grid_astar_theta_node.py
old mode 100644
new mode 100755
diff --git a/src/planner/scripts/lidar_health_check.py b/src/planner/scripts/lidar_health_check.py
new file mode 100755
index 0000000..72b0ec8
--- /dev/null
+++ b/src/planner/scripts/lidar_health_check.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""Check whether LaserScan remains usable despite driver console warnings."""
+
+import math
+import sys
+import time
+
+import rclpy
+from rclpy.node import Node
+from sensor_msgs.msg import LaserScan
+
+
+class LidarHealthCheck(Node):
+ def __init__(self):
+ super().__init__("planner_lidar_health_check")
+ self.declare_parameter("scan_topic", "/scan")
+ self.declare_parameter("duration_sec", 10.0)
+ self.declare_parameter("min_rate_hz", 5.0)
+ self.declare_parameter("min_valid_fraction", 0.02)
+ self.scan_topic = str(self.get_parameter("scan_topic").value)
+ self.duration_sec = max(2.0, float(self.get_parameter("duration_sec").value))
+ self.min_rate_hz = max(0.1, float(self.get_parameter("min_rate_hz").value))
+ self.min_valid_fraction = max(
+ 0.0, min(1.0, float(self.get_parameter("min_valid_fraction").value))
+ )
+ self.start_monotonic = time.monotonic()
+ self.receipt_times = []
+ self.valid_fractions = []
+ self.frame_ids = set()
+ self.subscription = self.create_subscription(
+ LaserScan, self.scan_topic, self._scan_cb, 20
+ )
+
+ def _scan_cb(self, msg):
+ self.receipt_times.append(time.monotonic())
+ self.frame_ids.add(msg.header.frame_id)
+ if msg.ranges:
+ valid = sum(
+ 1
+ for value in msg.ranges
+ if math.isfinite(value) and msg.range_min <= value <= msg.range_max
+ )
+ self.valid_fractions.append(valid / len(msg.ranges))
+
+ def complete(self):
+ return time.monotonic() - self.start_monotonic >= self.duration_sec
+
+ def evaluate(self):
+ elapsed = time.monotonic() - self.start_monotonic
+ if len(self.receipt_times) >= 2:
+ span = self.receipt_times[-1] - self.receipt_times[0]
+ rate = (len(self.receipt_times) - 1) / span if span > 0.0 else 0.0
+ else:
+ rate = 0.0
+ valid_mean = (
+ sum(self.valid_fractions) / len(self.valid_fractions)
+ if self.valid_fractions
+ else 0.0
+ )
+ errors = []
+ if not self.receipt_times:
+ errors.append(f"no {self.scan_topic} received in {elapsed:.1f} s")
+ if rate < self.min_rate_hz:
+ errors.append(f"scan rate {rate:.2f} Hz is below {self.min_rate_hz:.2f} Hz")
+ if valid_mean < self.min_valid_fraction:
+ errors.append(
+ f"valid range fraction {valid_mean:.3f} is below "
+ f"{self.min_valid_fraction:.3f}"
+ )
+ if not self.frame_ids or "" in self.frame_ids:
+ errors.append("LaserScan frame_id is empty")
+
+ self.get_logger().info(
+ f"scans={len(self.receipt_times)} rate={rate:.2f} Hz "
+ f"valid_fraction={valid_mean:.3f} frames={sorted(self.frame_ids)}"
+ )
+ for error in errors:
+ self.get_logger().error(error)
+ if not errors:
+ self.get_logger().info("PASS: lidar data is usable by AMCL")
+ return not errors
+
+
+def main():
+ rclpy.init()
+ node = LidarHealthCheck()
+ try:
+ while rclpy.ok() and not node.complete():
+ rclpy.spin_once(node, timeout_sec=0.1)
+ success = node.evaluate()
+ finally:
+ node.destroy_node()
+ rclpy.shutdown()
+ sys.exit(0 if success else 1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/planner/scripts/map_info.py b/src/planner/scripts/map_info.py
new file mode 100755
index 0000000..7cf3b96
--- /dev/null
+++ b/src/planner/scripts/map_info.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""Inspect a ROS map YAML/PGM pair and validate its metric dimensions."""
+
+import argparse
+from pathlib import Path
+import sys
+
+import yaml
+
+
+def _pgm_token(stream):
+ while True:
+ byte = stream.read(1)
+ if not byte:
+ raise ValueError("unexpected end of PGM header")
+ if byte.isspace():
+ continue
+ if byte == b"#":
+ stream.readline()
+ continue
+ token = byte
+ while True:
+ byte = stream.read(1)
+ if not byte or byte.isspace():
+ return token.decode("ascii")
+ token += byte
+
+
+def read_pgm_header(path):
+ with path.open("rb") as stream:
+ magic = _pgm_token(stream)
+ if magic not in ("P2", "P5"):
+ raise ValueError(f"unsupported image format {magic}, expected P2/P5")
+ width = int(_pgm_token(stream))
+ height = int(_pgm_token(stream))
+ max_value = int(_pgm_token(stream))
+ return magic, width, height, max_value
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("map_yaml", type=Path)
+ parser.add_argument("--expected-width", type=float, default=5.0)
+ parser.add_argument("--expected-height", type=float, default=5.0)
+ parser.add_argument("--tolerance", type=float, default=0.15)
+ parser.add_argument("--no-check", action="store_true")
+ args = parser.parse_args()
+
+ data = yaml.safe_load(args.map_yaml.read_text(encoding="utf-8")) or {}
+ image = Path(data["image"])
+ if not image.is_absolute():
+ image = args.map_yaml.parent / image
+ magic, pixels_x, pixels_y, max_value = read_pgm_header(image)
+ resolution = float(data["resolution"])
+ metric_x = pixels_x * resolution
+ metric_y = pixels_y * resolution
+ origin = data.get("origin", [0.0, 0.0, 0.0])
+
+ print(f"yaml: {args.map_yaml}")
+ print(f"image: {image}")
+ print(f"format: {magic}, max_value={max_value}")
+ print(f"pixels: {pixels_x} x {pixels_y}")
+ print(f"resolution: {resolution:.6f} m/pixel")
+ print(f"size: {metric_x:.3f} x {metric_y:.3f} m")
+ print(f"origin: {origin}")
+
+ if args.no_check:
+ return 0
+ if (
+ abs(metric_x - args.expected_width) > args.tolerance
+ or abs(metric_y - args.expected_height) > args.tolerance
+ ):
+ print(
+ "FAIL: expected %.3f x %.3f m +/- %.3f m"
+ % (args.expected_width, args.expected_height, args.tolerance)
+ )
+ return 1
+ print("PASS: map dimensions match the expected metric size")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/src/planner/scripts/navigation_test_recorder.py b/src/planner/scripts/navigation_test_recorder.py
new file mode 100755
index 0000000..254e330
--- /dev/null
+++ b/src/planner/scripts/navigation_test_recorder.py
@@ -0,0 +1,460 @@
+#!/usr/bin/env python3
+"""Record and summarize real-robot planner and tracker performance."""
+
+import csv
+import json
+import math
+from pathlib import Path
+import statistics
+import time
+
+import rclpy
+from ackermann_msgs.msg import AckermannDriveStamped
+from geometry_msgs.msg import PoseStamped, Twist
+from nav_msgs.msg import OccupancyGrid, Odometry, Path as NavPath
+from rclpy.duration import Duration
+from rclpy.node import Node
+from rclpy.qos import DurabilityPolicy, QoSProfile, ReliabilityPolicy
+from rclpy.time import Time
+from std_msgs.msg import Bool, String
+from tf2_ros import Buffer, TransformException, TransformListener
+
+
+def yaw_from_quaternion(q):
+ return math.atan2(
+ 2.0 * (q.w * q.z + q.x * q.y),
+ 1.0 - 2.0 * (q.y * q.y + q.z * q.z),
+ )
+
+
+def normalize_angle(angle):
+ return math.atan2(math.sin(angle), math.cos(angle))
+
+
+def percentile(values, fraction):
+ if not values:
+ return None
+ ordered = sorted(values)
+ index = min(len(ordered) - 1, max(0, math.ceil(fraction * len(ordered)) - 1))
+ return ordered[index]
+
+
+def point_segment_distance(px, py, ax, ay, bx, by):
+ dx = bx - ax
+ dy = by - ay
+ length_sq = dx * dx + dy * dy
+ if length_sq <= 1e-12:
+ return math.hypot(px - ax, py - ay)
+ ratio = ((px - ax) * dx + (py - ay) * dy) / length_sq
+ ratio = max(0.0, min(1.0, ratio))
+ cx = ax + ratio * dx
+ cy = ay + ratio * dy
+ return math.hypot(px - cx, py - cy)
+
+
+class NavigationTestRecorder(Node):
+ def __init__(self):
+ super().__init__("navigation_test_recorder")
+ self.declare_parameter("test_name", "navigation_test")
+ self.declare_parameter("output_dir", "~/.ros/navigation_tests")
+ self.declare_parameter("map_frame", "map")
+ self.declare_parameter("map_topic", "/map")
+ self.declare_parameter("odom_topic", "/odom_combined")
+ self.declare_parameter("goal_pose_topic", "/goal_pose")
+ self.declare_parameter("plan_topic", "/plan")
+ self.declare_parameter("cmd_vel_topic", "/cmd_vel")
+ self.declare_parameter("drive_command_topic", "/ackermann_cmd")
+ self.declare_parameter("cancel_topic", "/navigation_cancel")
+ self.declare_parameter("planner_status_topic", "/hybrid_astar_status")
+ self.declare_parameter("tracker_status_topic", "/pure_pursuit_status")
+ self.declare_parameter("sample_rate", 20.0)
+ self.declare_parameter("tf_timeout", 0.10)
+ self.declare_parameter("occupied_threshold", 50)
+
+ self.test_name = str(self.get_parameter("test_name").value)
+ output_dir = Path(str(self.get_parameter("output_dir").value)).expanduser()
+ output_dir.mkdir(parents=True, exist_ok=True)
+ timestamp = time.strftime("%Y%m%d_%H%M%S")
+ self.output_base = output_dir / f"{self.test_name}_{timestamp}"
+ self.csv_path = self.output_base.with_suffix(".csv")
+ self.summary_path = self.output_base.with_suffix(".json")
+ self.map_frame = str(self.get_parameter("map_frame").value)
+ self.map_topic = str(self.get_parameter("map_topic").value)
+ self.odom_topic = str(self.get_parameter("odom_topic").value)
+ self.goal_pose_topic = str(self.get_parameter("goal_pose_topic").value)
+ self.plan_topic = str(self.get_parameter("plan_topic").value)
+ self.cmd_vel_topic = str(self.get_parameter("cmd_vel_topic").value)
+ self.drive_command_topic = str(
+ self.get_parameter("drive_command_topic").value
+ )
+ self.cancel_topic = str(self.get_parameter("cancel_topic").value)
+ self.planner_status_topic = str(
+ self.get_parameter("planner_status_topic").value
+ )
+ self.tracker_status_topic = str(
+ self.get_parameter("tracker_status_topic").value
+ )
+ self.tf_timeout = float(self.get_parameter("tf_timeout").value)
+ self.occupied_threshold = int(self.get_parameter("occupied_threshold").value)
+
+ self.started_at = self.get_clock().now()
+ self.latest_odom = None
+ self.latest_cmd = Twist()
+ self.latest_drive = AckermannDriveStamped()
+ self.latest_path = []
+ self.latest_goal = None
+ self.map_msg = None
+ self.planner_status = ""
+ self.tracker_status = ""
+ self.pending_goal_time = None
+ self.pending_goal_xy = None
+ self.plan_latencies = []
+ self.cancel_stop_latencies = []
+ self.cancel_pending_time = None
+ self.plan_count = 0
+ self.plan_ok_count = 0
+ self.plan_fail_count = 0
+ self.plan_collision_cells = []
+ self.plan_min_turning_radii = []
+ self.plan_lengths = []
+ self.reverse_fractions = []
+ self.cross_track_errors = []
+ self.goal_errors = []
+ self.samples = 0
+ self.transform_failures = 0
+ self.max_abs_cmd_linear = 0.0
+ self.max_abs_cmd_angular = 0.0
+ self.max_abs_drive_speed = 0.0
+ self.max_abs_steering_angle = 0.0
+ self.max_abs_measured_linear = 0.0
+ self.max_abs_measured_angular = 0.0
+ self.tf_buffer = Buffer()
+ self.tf_listener = TransformListener(self.tf_buffer, self)
+
+ self.csv_file = self.csv_path.open("w", newline="", encoding="utf-8")
+ self.csv_writer = csv.writer(self.csv_file)
+ self.csv_writer.writerow([
+ "elapsed_sec", "map_x", "map_y", "map_yaw",
+ "measured_linear", "measured_angular",
+ "cmd_linear", "cmd_angular", "drive_speed", "steering_angle",
+ "cross_track_error",
+ "goal_error", "plan_points", "planner_status", "tracker_status",
+ ])
+
+ map_qos = QoSProfile(
+ depth=1,
+ reliability=ReliabilityPolicy.RELIABLE,
+ durability=DurabilityPolicy.TRANSIENT_LOCAL,
+ )
+ self.input_subscriptions = [
+ self.create_subscription(
+ OccupancyGrid, self.map_topic, self._map_cb, map_qos
+ ),
+ self.create_subscription(Odometry, self.odom_topic, self._odom_cb, 20),
+ self.create_subscription(
+ PoseStamped, self.goal_pose_topic, self._goal_cb, map_qos
+ ),
+ self.create_subscription(NavPath, self.plan_topic, self._plan_cb, 10),
+ self.create_subscription(Twist, self.cmd_vel_topic, self._cmd_cb, 20),
+ self.create_subscription(
+ AckermannDriveStamped, self.drive_command_topic, self._drive_cb, 20
+ ),
+ self.create_subscription(Bool, self.cancel_topic, self._cancel_cb, 10),
+ self.create_subscription(
+ String, self.planner_status_topic, self._planner_status_cb, 20
+ ),
+ self.create_subscription(
+ String, self.tracker_status_topic, self._tracker_status_cb, 20
+ ),
+ ]
+ sample_rate = max(1.0, float(self.get_parameter("sample_rate").value))
+ self.timer = self.create_timer(1.0 / sample_rate, self._sample)
+ self.get_logger().info(f"recording navigation test to {self.csv_path}")
+
+ def _map_cb(self, msg):
+ self.map_msg = msg
+
+ def _odom_cb(self, msg):
+ self.latest_odom = msg
+
+ def _cmd_cb(self, msg):
+ self.latest_cmd = msg
+ self.max_abs_cmd_linear = max(self.max_abs_cmd_linear, abs(msg.linear.x))
+ self.max_abs_cmd_angular = max(self.max_abs_cmd_angular, abs(msg.angular.z))
+ if (
+ self.cancel_pending_time is not None
+ and abs(msg.linear.x) <= 0.01
+ and abs(msg.angular.z) <= 0.01
+ ):
+ latency = (self.get_clock().now() - self.cancel_pending_time).nanoseconds * 1e-9
+ self.cancel_stop_latencies.append(max(0.0, latency))
+ self.cancel_pending_time = None
+
+ def _cancel_cb(self, msg):
+ if not msg.data:
+ return
+ if abs(self.latest_cmd.linear.x) <= 0.01 and abs(self.latest_cmd.angular.z) <= 0.01:
+ self.cancel_stop_latencies.append(0.0)
+ else:
+ self.cancel_pending_time = self.get_clock().now()
+
+ def _drive_cb(self, msg):
+ self.latest_drive = msg
+ self.max_abs_drive_speed = max(
+ self.max_abs_drive_speed, abs(msg.drive.speed)
+ )
+ self.max_abs_steering_angle = max(
+ self.max_abs_steering_angle, abs(msg.drive.steering_angle)
+ )
+
+ def _goal_cb(self, msg):
+ goal_xy = (msg.pose.position.x, msg.pose.position.y)
+ if self.pending_goal_xy is None or math.hypot(
+ goal_xy[0] - self.pending_goal_xy[0],
+ goal_xy[1] - self.pending_goal_xy[1],
+ ) > 1e-4:
+ self.pending_goal_time = self.get_clock().now()
+ self.pending_goal_xy = goal_xy
+ self.latest_goal = goal_xy
+
+ def _planner_status_cb(self, msg):
+ self.planner_status = msg.data
+ if msg.data.startswith("plan_ok"):
+ self.plan_ok_count += 1
+ elif msg.data.startswith("plan_failed") or msg.data.startswith("plan_rejected"):
+ self.plan_fail_count += 1
+
+ def _tracker_status_cb(self, msg):
+ self.tracker_status = msg.data
+
+ def _plan_cb(self, msg):
+ if len(msg.poses) < 2:
+ self.latest_path = []
+ return
+ self.latest_path = [
+ (
+ pose.pose.position.x,
+ pose.pose.position.y,
+ yaw_from_quaternion(pose.pose.orientation),
+ )
+ for pose in msg.poses
+ ]
+ self.plan_count += 1
+ if self.pending_goal_time is not None:
+ latency = (self.get_clock().now() - self.pending_goal_time).nanoseconds * 1e-9
+ self.plan_latencies.append(max(0.0, latency))
+ self.pending_goal_time = None
+ self.plan_lengths.append(self._path_length(self.latest_path))
+ self.plan_min_turning_radii.append(self._minimum_turning_radius(self.latest_path))
+ self.reverse_fractions.append(self._reverse_fraction(self.latest_path))
+ self.plan_collision_cells.append(self._occupied_path_points(self.latest_path))
+
+ def _current_map_pose(self):
+ if self.latest_odom is None:
+ return None
+ msg = self.latest_odom
+ x = msg.pose.pose.position.x
+ y = msg.pose.pose.position.y
+ yaw = yaw_from_quaternion(msg.pose.pose.orientation)
+ source_frame = msg.header.frame_id or self.map_frame
+ if source_frame == self.map_frame:
+ return x, y, yaw
+ try:
+ transform = self.tf_buffer.lookup_transform(
+ self.map_frame,
+ source_frame,
+ Time(),
+ timeout=Duration(seconds=self.tf_timeout),
+ )
+ except TransformException:
+ self.transform_failures += 1
+ return None
+ t = transform.transform.translation
+ transform_yaw = yaw_from_quaternion(transform.transform.rotation)
+ cos_yaw = math.cos(transform_yaw)
+ sin_yaw = math.sin(transform_yaw)
+ return (
+ t.x + cos_yaw * x - sin_yaw * y,
+ t.y + sin_yaw * x + cos_yaw * y,
+ normalize_angle(transform_yaw + yaw),
+ )
+
+ def _sample(self):
+ pose = self._current_map_pose()
+ if pose is None or self.latest_odom is None:
+ return
+ x, y, yaw = pose
+ measured_linear = self.latest_odom.twist.twist.linear.x
+ measured_angular = self.latest_odom.twist.twist.angular.z
+ self.max_abs_measured_linear = max(
+ self.max_abs_measured_linear, abs(measured_linear)
+ )
+ self.max_abs_measured_angular = max(
+ self.max_abs_measured_angular, abs(measured_angular)
+ )
+ cross_track = self._cross_track_error(x, y)
+ goal_error = None
+ if self.latest_goal is not None:
+ goal_error = math.hypot(self.latest_goal[0] - x, self.latest_goal[1] - y)
+ self.goal_errors.append(goal_error)
+ if cross_track is not None:
+ self.cross_track_errors.append(cross_track)
+ elapsed = (self.get_clock().now() - self.started_at).nanoseconds * 1e-9
+ self.csv_writer.writerow([
+ f"{elapsed:.6f}", f"{x:.6f}", f"{y:.6f}", f"{yaw:.6f}",
+ f"{measured_linear:.6f}", f"{measured_angular:.6f}",
+ f"{self.latest_cmd.linear.x:.6f}", f"{self.latest_cmd.angular.z:.6f}",
+ f"{self.latest_drive.drive.speed:.6f}",
+ f"{self.latest_drive.drive.steering_angle:.6f}",
+ "" if cross_track is None else f"{cross_track:.6f}",
+ "" if goal_error is None else f"{goal_error:.6f}",
+ len(self.latest_path), self.planner_status, self.tracker_status,
+ ])
+ self.samples += 1
+ if self.samples % 20 == 0:
+ self.csv_file.flush()
+
+ def _cross_track_error(self, x, y):
+ if len(self.latest_path) < 2:
+ return None
+ return min(
+ point_segment_distance(x, y, a[0], a[1], b[0], b[1])
+ for a, b in zip(self.latest_path, self.latest_path[1:])
+ )
+
+ @staticmethod
+ def _path_length(path):
+ return sum(
+ math.hypot(b[0] - a[0], b[1] - a[1])
+ for a, b in zip(path, path[1:])
+ )
+
+ @staticmethod
+ def _minimum_turning_radius(path):
+ max_curvature = 0.0
+ for a, b in zip(path, path[1:]):
+ distance = math.hypot(b[0] - a[0], b[1] - a[1])
+ if distance <= 1e-6:
+ continue
+ curvature = abs(normalize_angle(b[2] - a[2])) / distance
+ max_curvature = max(max_curvature, curvature)
+ return None if max_curvature <= 1e-9 else 1.0 / max_curvature
+
+ @staticmethod
+ def _reverse_fraction(path):
+ reverse_length = 0.0
+ total_length = 0.0
+ for a, b in zip(path, path[1:]):
+ dx = b[0] - a[0]
+ dy = b[1] - a[1]
+ distance = math.hypot(dx, dy)
+ if distance <= 1e-6:
+ continue
+ segment_yaw = math.atan2(dy, dx)
+ if math.cos(normalize_angle(a[2] - segment_yaw)) < 0.0:
+ reverse_length += distance
+ total_length += distance
+ return 0.0 if total_length <= 1e-9 else reverse_length / total_length
+
+ def _occupied_path_points(self, path):
+ msg = self.map_msg
+ if msg is None or msg.info.resolution <= 0.0:
+ return None
+ origin_yaw = yaw_from_quaternion(msg.info.origin.orientation)
+ cos_yaw = math.cos(origin_yaw)
+ sin_yaw = math.sin(origin_yaw)
+ collisions = 0
+ for x, y, _ in path:
+ dx = x - msg.info.origin.position.x
+ dy = y - msg.info.origin.position.y
+ mx = cos_yaw * dx + sin_yaw * dy
+ my = -sin_yaw * dx + cos_yaw * dy
+ gx = math.floor(mx / msg.info.resolution)
+ gy = math.floor(my / msg.info.resolution)
+ if gx < 0 or gy < 0 or gx >= msg.info.width or gy >= msg.info.height:
+ collisions += 1
+ continue
+ value = msg.data[gy * msg.info.width + gx]
+ if value < 0 or value >= self.occupied_threshold:
+ collisions += 1
+ return collisions
+
+ @staticmethod
+ def _stats(values):
+ if not values:
+ return {"count": 0, "mean": None, "rms": None, "p95": None, "max": None}
+ return {
+ "count": len(values),
+ "mean": statistics.fmean(values),
+ "rms": math.sqrt(statistics.fmean(value * value for value in values)),
+ "p95": percentile(values, 0.95),
+ "max": max(values),
+ }
+
+ def write_summary(self):
+ duration = (self.get_clock().now() - self.started_at).nanoseconds * 1e-9
+ valid_radii = [value for value in self.plan_min_turning_radii if value is not None]
+ valid_collisions = [value for value in self.plan_collision_cells if value is not None]
+ summary = {
+ "test_name": self.test_name,
+ "duration_sec": duration,
+ "samples": self.samples,
+ "plan_count": self.plan_count,
+ "plan_ok_status_count": self.plan_ok_count,
+ "plan_fail_status_count": self.plan_fail_count,
+ "planning_latency_sec": self._stats(self.plan_latencies),
+ "cancel_to_zero_command_sec": self._stats(self.cancel_stop_latencies),
+ "path_length_m": self._stats(self.plan_lengths),
+ "path_occupied_points_max": max(valid_collisions) if valid_collisions else None,
+ "minimum_planned_turning_radius_m": min(valid_radii) if valid_radii else None,
+ "reverse_fraction": self._stats(self.reverse_fractions),
+ "cross_track_error_m": self._stats(self.cross_track_errors),
+ "goal_error_m": self._stats(self.goal_errors),
+ "final_goal_error_m": self.goal_errors[-1] if self.goal_errors else None,
+ "minimum_goal_error_m": min(self.goal_errors) if self.goal_errors else None,
+ "transform_failures": self.transform_failures,
+ "max_abs_cmd_linear": self.max_abs_cmd_linear,
+ "max_abs_cmd_angular": self.max_abs_cmd_angular,
+ "max_abs_drive_speed": self.max_abs_drive_speed,
+ "max_abs_steering_angle": self.max_abs_steering_angle,
+ "max_abs_measured_linear": self.max_abs_measured_linear,
+ "max_abs_measured_angular": self.max_abs_measured_angular,
+ "last_planner_status": self.planner_status,
+ "last_tracker_status": self.tracker_status,
+ "csv": str(self.csv_path),
+ }
+ self.csv_file.flush()
+ self.summary_path.write_text(
+ json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8"
+ )
+ self.get_logger().info(f"wrote navigation summary to {self.summary_path}")
+
+ def close(self):
+ if not self.csv_file.closed:
+ self.write_summary()
+ self.csv_file.close()
+
+
+def main():
+ rclpy.init()
+ node = NavigationTestRecorder()
+ try:
+ rclpy.spin(node)
+ except KeyboardInterrupt:
+ pass
+ finally:
+ node.close()
+ try:
+ if rclpy.ok():
+ node.destroy_node()
+ except Exception:
+ pass
+ try:
+ if rclpy.ok():
+ rclpy.shutdown()
+ except Exception:
+ pass
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/planner/scripts/odom_map_tf.py b/src/planner/scripts/odom_map_tf.py
new file mode 100755
index 0000000..938f70d
--- /dev/null
+++ b/src/planner/scripts/odom_map_tf.py
@@ -0,0 +1,249 @@
+#!/usr/bin/env python3
+"""Align map to wheel odom using a user-provided initial pose.
+
+This node replaces AMCL for controlled short-range tests. It publishes a fixed
+map->odom transform computed from:
+
+ known current map pose * inverse(current odom pose)
+
+After initialization, pose updates come only from /odom.
+"""
+
+import math
+
+import rclpy
+from geometry_msgs.msg import PoseWithCovarianceStamped, TransformStamped
+from nav_msgs.msg import Odometry
+from rclpy.node import Node
+from std_msgs.msg import String
+from tf2_ros import TransformBroadcaster
+
+
+def yaw_from_quaternion(q):
+ return math.atan2(
+ 2.0 * (q.w * q.z + q.x * q.y),
+ 1.0 - 2.0 * (q.y * q.y + q.z * q.z),
+ )
+
+
+def normalize_angle(angle):
+ return math.atan2(math.sin(angle), math.cos(angle))
+
+
+def fill_yaw_quaternion(q, yaw):
+ q.x = 0.0
+ q.y = 0.0
+ q.z = math.sin(0.5 * yaw)
+ q.w = math.cos(0.5 * yaw)
+
+
+class OdomMapTf(Node):
+ def __init__(self):
+ super().__init__("planner_odom_map_tf")
+ self.declare_parameter("map_frame", "map")
+ self.declare_parameter("odom_frame", "odom")
+ self.declare_parameter("odom_topic", "/odom")
+ self.declare_parameter("initial_pose_topic", "/initialpose")
+ self.declare_parameter("status_topic", "/odom_localization_status")
+ self.declare_parameter("initial_x", 0.0)
+ self.declare_parameter("initial_y", 0.0)
+ self.declare_parameter("initial_yaw", 0.0)
+ self.declare_parameter("initialize_on_first_odom", True)
+ self.declare_parameter("publish_rate", 30.0)
+ self.declare_parameter("state_log_period", 1.0)
+
+ self.map_frame = str(self.get_parameter("map_frame").value)
+ self.default_odom_frame = str(self.get_parameter("odom_frame").value)
+ self.active_odom_frame = self.default_odom_frame
+ self.initialize_on_first_odom = bool(
+ self.get_parameter("initialize_on_first_odom").value
+ )
+ self.initial_pose = (
+ float(self.get_parameter("initial_x").value),
+ float(self.get_parameter("initial_y").value),
+ float(self.get_parameter("initial_yaw").value),
+ )
+ self.state_log_period = max(
+ 0.0, float(self.get_parameter("state_log_period").value)
+ )
+
+ self.latest_odom_pose = None
+ self.pending_initial_pose = None
+ self.have_transform = False
+ self.last_state_log_ns = 0
+ self.map_to_odom_x = 0.0
+ self.map_to_odom_y = 0.0
+ self.map_to_odom_yaw = 0.0
+
+ self.tf_broadcaster = TransformBroadcaster(self)
+ self.status_pub = self.create_publisher(
+ String, str(self.get_parameter("status_topic").value), 10
+ )
+ self.odom_sub = self.create_subscription(
+ Odometry,
+ str(self.get_parameter("odom_topic").value),
+ self._odom_callback,
+ 20,
+ )
+ self.initial_pose_sub = self.create_subscription(
+ PoseWithCovarianceStamped,
+ str(self.get_parameter("initial_pose_topic").value),
+ self._initial_pose_callback,
+ 10,
+ )
+
+ publish_rate = max(1.0, float(self.get_parameter("publish_rate").value))
+ self.timer = self.create_timer(1.0 / publish_rate, self._publish_transform)
+ self._publish_status(
+ "waiting_for_odom initial_pose=(%.3f, %.3f, %.3f)"
+ % self.initial_pose
+ )
+
+ def _odom_callback(self, msg):
+ source_frame = msg.header.frame_id or self.default_odom_frame
+ self.active_odom_frame = source_frame
+ self.latest_odom_pose = (
+ msg.pose.pose.position.x,
+ msg.pose.pose.position.y,
+ yaw_from_quaternion(msg.pose.pose.orientation),
+ )
+
+ if self.pending_initial_pose is not None:
+ pending = self.pending_initial_pose
+ self.pending_initial_pose = None
+ self._initialize_from_pose(*pending, reason="initialpose")
+ elif not self.have_transform and self.initialize_on_first_odom:
+ self._initialize_from_pose(*self.initial_pose, reason="launch_initial")
+
+ def _initial_pose_callback(self, msg):
+ frame_id = msg.header.frame_id or self.map_frame
+ if frame_id != self.map_frame:
+ self._publish_status(
+ "initialpose_rejected frame=%s expected=%s"
+ % (frame_id, self.map_frame)
+ )
+ return
+
+ pose = msg.pose.pose
+ initial_pose = (
+ pose.position.x,
+ pose.position.y,
+ yaw_from_quaternion(pose.orientation),
+ )
+ if self.latest_odom_pose is None:
+ self.pending_initial_pose = initial_pose
+ self._publish_status("initialpose_queued waiting_for_odom")
+ return
+ self._initialize_from_pose(*initial_pose, reason="initialpose")
+
+ def _initialize_from_pose(self, map_x, map_y, map_yaw, reason):
+ if self.latest_odom_pose is None:
+ self.pending_initial_pose = (map_x, map_y, map_yaw)
+ return
+
+ odom_x, odom_y, odom_yaw = self.latest_odom_pose
+ delta_yaw = normalize_angle(map_yaw - odom_yaw)
+ cos_yaw = math.cos(delta_yaw)
+ sin_yaw = math.sin(delta_yaw)
+ self.map_to_odom_x = map_x - (cos_yaw * odom_x - sin_yaw * odom_y)
+ self.map_to_odom_y = map_y - (sin_yaw * odom_x + cos_yaw * odom_y)
+ self.map_to_odom_yaw = delta_yaw
+ self.have_transform = True
+ self._publish_status(
+ "%s_aligned %s->%s x=%.3f y=%.3f yaw=%.3f"
+ % (
+ reason,
+ self.map_frame,
+ self.active_odom_frame,
+ self.map_to_odom_x,
+ self.map_to_odom_y,
+ self.map_to_odom_yaw,
+ )
+ )
+
+ def _publish_transform(self):
+ if not self.have_transform:
+ self._maybe_log_state()
+ return
+ msg = TransformStamped()
+ msg.header.stamp = self.get_clock().now().to_msg()
+ msg.header.frame_id = self.map_frame
+ msg.child_frame_id = self.active_odom_frame
+ msg.transform.translation.x = self.map_to_odom_x
+ msg.transform.translation.y = self.map_to_odom_y
+ msg.transform.translation.z = 0.0
+ fill_yaw_quaternion(msg.transform.rotation, self.map_to_odom_yaw)
+ self.tf_broadcaster.sendTransform(msg)
+ self._maybe_log_state()
+
+ def _maybe_log_state(self):
+ if self.state_log_period <= 0.0:
+ return
+ now_ns = self.get_clock().now().nanoseconds
+ min_period_ns = int(self.state_log_period * 1_000_000_000)
+ if now_ns - self.last_state_log_ns < min_period_ns:
+ return
+ self.last_state_log_ns = now_ns
+
+ if self.latest_odom_pose is None:
+ self.get_logger().info(
+ "odom_tf_state waiting_for_odom "
+ "initial_pose=(%.3f,%.3f,%.3f)"
+ % self.initial_pose
+ )
+ return
+
+ odom_x, odom_y, odom_yaw = self.latest_odom_pose
+ if not self.have_transform:
+ self.get_logger().info(
+ "odom_tf_state waiting_for_alignment "
+ "odom=(%.3f,%.3f,%.3f) pending_initialpose=%s"
+ % (odom_x, odom_y, odom_yaw, self.pending_initial_pose is not None)
+ )
+ return
+
+ cos_yaw = math.cos(self.map_to_odom_yaw)
+ sin_yaw = math.sin(self.map_to_odom_yaw)
+ map_x = self.map_to_odom_x + cos_yaw * odom_x - sin_yaw * odom_y
+ map_y = self.map_to_odom_y + sin_yaw * odom_x + cos_yaw * odom_y
+ map_yaw = normalize_angle(self.map_to_odom_yaw + odom_yaw)
+ self.get_logger().info(
+ "odom_tf_state aligned=%s odom=(%.3f,%.3f,%.3f) "
+ "map_pose=(%.3f,%.3f,%.3f) map_to_odom=(%.3f,%.3f,%.3f)"
+ % (
+ self.have_transform,
+ odom_x,
+ odom_y,
+ odom_yaw,
+ map_x,
+ map_y,
+ map_yaw,
+ self.map_to_odom_x,
+ self.map_to_odom_y,
+ self.map_to_odom_yaw,
+ )
+ )
+
+ def _publish_status(self, text):
+ msg = String()
+ msg.data = text
+ self.status_pub.publish(msg)
+ self.get_logger().info(text)
+
+
+def main(args=None):
+ rclpy.init(args=args)
+ node = None
+ try:
+ node = OdomMapTf()
+ rclpy.spin(node)
+ except KeyboardInterrupt:
+ pass
+ finally:
+ if node is not None:
+ node.destroy_node()
+ rclpy.shutdown()
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/planner/scripts/preflight_check.py b/src/planner/scripts/preflight_check.py
new file mode 100755
index 0000000..345d2d9
--- /dev/null
+++ b/src/planner/scripts/preflight_check.py
@@ -0,0 +1,235 @@
+#!/usr/bin/env python3
+"""Validate the real-robot navigation interfaces before sending a goal."""
+
+import sys
+import time
+
+import rclpy
+from nav_msgs.msg import OccupancyGrid, Odometry
+from rclpy.duration import Duration
+from rclpy.node import Node
+from rclpy.qos import DurabilityPolicy, QoSProfile, ReliabilityPolicy
+from rclpy.time import Time
+from sensor_msgs.msg import LaserScan
+from tf2_ros import Buffer, TransformListener
+
+
+class PreflightCheck(Node):
+ def __init__(self):
+ super().__init__("hybrid_astar_preflight")
+ self.declare_parameter("expected_map_width", 5.0)
+ self.declare_parameter("expected_map_height", 5.0)
+ self.declare_parameter("map_size_tolerance", 0.15)
+ self.declare_parameter("timeout_sec", 10.0)
+ self.declare_parameter("require_map", True)
+ self.declare_parameter("require_scan", True)
+ self.declare_parameter("require_tf", True)
+ self.declare_parameter("require_odom_tf", False)
+ self.declare_parameter("require_base_tf", True)
+ self.declare_parameter("map_frame", "map")
+ self.declare_parameter("odom_frame", "odom")
+ self.declare_parameter("base_frame", "base_footprint")
+ self.declare_parameter("map_topic", "/map")
+ self.declare_parameter("odom_topic", "/odom_combined")
+ self.declare_parameter("scan_topic", "/scan")
+ self.declare_parameter("cmd_vel_topic", "/cmd_vel")
+ self.declare_parameter("expected_scan_frame", "laser_link")
+ self.declare_parameter("tf_wait_sec", 3.0)
+ self.declare_parameter("drive_command_topic", "/ackermann_cmd")
+
+ self.require_map = bool(self.get_parameter("require_map").value)
+ self.require_scan = bool(self.get_parameter("require_scan").value)
+ self.require_tf = bool(self.get_parameter("require_tf").value)
+ self.require_odom_tf = bool(self.get_parameter("require_odom_tf").value)
+ self.require_base_tf = bool(self.get_parameter("require_base_tf").value)
+ self.map_topic = str(self.get_parameter("map_topic").value)
+ self.odom_topic = str(self.get_parameter("odom_topic").value)
+ self.scan_topic = str(self.get_parameter("scan_topic").value)
+ self.cmd_vel_topic = str(self.get_parameter("cmd_vel_topic").value)
+
+ map_qos = QoSProfile(
+ depth=1,
+ reliability=ReliabilityPolicy.RELIABLE,
+ durability=DurabilityPolicy.TRANSIENT_LOCAL,
+ )
+
+ self.map_msg = None
+ self.odom_msg = None
+ self.scan_msg = None
+ self.create_subscription(OccupancyGrid, self.map_topic, self._map_cb, map_qos)
+ self.create_subscription(Odometry, self.odom_topic, self._odom_cb, 10)
+ self.create_subscription(LaserScan, self.scan_topic, self._scan_cb, 10)
+ self.tf_buffer = Buffer()
+ self.tf_listener = TransformListener(self.tf_buffer, self)
+
+ def _map_cb(self, msg):
+ self.map_msg = msg
+
+ def _odom_cb(self, msg):
+ self.odom_msg = msg
+
+ def _scan_cb(self, msg):
+ self.scan_msg = msg
+
+ def wait_for_inputs(self):
+ deadline = time.monotonic() + float(self.get_parameter("timeout_sec").value)
+ while rclpy.ok() and time.monotonic() < deadline:
+ rclpy.spin_once(self, timeout_sec=0.1)
+ map_ready = self.map_msg is not None or not self.require_map
+ odom_ready = self.odom_msg is not None
+ scan_ready = self.scan_msg is not None or not self.require_scan
+ if map_ready and odom_ready and scan_ready:
+ return
+
+ def evaluate(self):
+ errors = []
+ if self.map_msg is None:
+ if self.require_map:
+ errors.append("no %s OccupancyGrid received" % self.map_topic)
+ else:
+ map_frame = str(self.get_parameter("map_frame").value)
+ if (
+ self.map_msg.header.frame_id
+ and self.map_msg.header.frame_id != map_frame
+ ):
+ errors.append(
+ "/map frame is %s, expected %s"
+ % (self.map_msg.header.frame_id, map_frame)
+ )
+ width = self.map_msg.info.width * self.map_msg.info.resolution
+ height = self.map_msg.info.height * self.map_msg.info.resolution
+ expected_width = float(self.get_parameter("expected_map_width").value)
+ expected_height = float(self.get_parameter("expected_map_height").value)
+ tolerance = float(self.get_parameter("map_size_tolerance").value)
+ self.get_logger().info(
+ f"map size {width:.3f} x {height:.3f} m, "
+ f"resolution {self.map_msg.info.resolution:.3f} m"
+ )
+ if (
+ abs(width - expected_width) > tolerance
+ or abs(height - expected_height) > tolerance
+ ):
+ errors.append(
+ "map is not the expected %.2f x %.2f m (tolerance %.2f m)"
+ % (expected_width, expected_height, tolerance)
+ )
+
+ if self.odom_msg is None:
+ errors.append("no %s received" % self.odom_topic)
+ if self.scan_msg is None:
+ if self.require_scan:
+ errors.append("no %s received" % self.scan_topic)
+ elif not self.scan_msg.header.frame_id:
+ errors.append("%s frame_id is empty" % self.scan_topic)
+ else:
+ expected_scan_frame = str(
+ self.get_parameter("expected_scan_frame").value
+ )
+ if (
+ expected_scan_frame
+ and self.scan_msg.header.frame_id != expected_scan_frame
+ ):
+ errors.append(
+ "%s frame is %s, expected %s"
+ % (self.scan_topic, self.scan_msg.header.frame_id, expected_scan_frame)
+ )
+
+ map_frame = str(self.get_parameter("map_frame").value)
+ odom_frame = str(self.get_parameter("odom_frame").value)
+ base_frame = str(self.get_parameter("base_frame").value)
+ if self.require_tf:
+ if self.require_odom_tf:
+ if not self._wait_for_tf(map_frame, odom_frame):
+ errors.append("TF unavailable: %s -> %s" % (map_frame, odom_frame))
+
+ if self.require_base_tf and not self._wait_for_tf(map_frame, base_frame):
+ errors.append("TF unavailable: %s -> %s" % (map_frame, base_frame))
+ elif (
+ self.require_base_tf
+ and self.scan_msg is not None
+ and self.scan_msg.header.frame_id
+ ):
+ scan_frame = self.scan_msg.header.frame_id
+ if not self._wait_for_tf(base_frame, scan_frame):
+ errors.append("TF unavailable: %s -> %s" % (base_frame, scan_frame))
+ elif self.require_scan and self.scan_msg is not None and self.scan_msg.header.frame_id:
+ scan_frame = self.scan_msg.header.frame_id
+ self.get_logger().info("scan frame check only: %s" % scan_frame)
+
+ publisher_deadline = time.monotonic() + 1.5
+ cmd_publishers = self.get_publishers_info_by_topic(self.cmd_vel_topic)
+ while not cmd_publishers and time.monotonic() < publisher_deadline:
+ rclpy.spin_once(self, timeout_sec=0.1)
+ cmd_publishers = self.get_publishers_info_by_topic(self.cmd_vel_topic)
+ if len(cmd_publishers) != 1:
+ names = [
+ "%s/%s" % (info.node_namespace, info.node_name)
+ for info in cmd_publishers
+ ]
+ errors.append(
+ "expected exactly one %s publisher, found %d: %s"
+ % (self.cmd_vel_topic, len(cmd_publishers), ", ".join(names) or "none")
+ )
+
+ drive_topic = str(self.get_parameter("drive_command_topic").value)
+ drive_publishers = self.get_publishers_info_by_topic(drive_topic)
+ drive_subscribers = self.get_subscriptions_info_by_topic(drive_topic)
+ if len(drive_publishers) != 1:
+ names = [
+ "%s/%s" % (info.node_namespace, info.node_name)
+ for info in drive_publishers
+ ]
+ errors.append(
+ "expected exactly one %s publisher, found %d: %s"
+ % (drive_topic, len(drive_publishers), ", ".join(names) or "none")
+ )
+ if len(drive_subscribers) != 1:
+ names = [
+ "%s/%s" % (info.node_namespace, info.node_name)
+ for info in drive_subscribers
+ ]
+ errors.append(
+ "expected exactly one %s subscriber, found %d: %s"
+ % (drive_topic, len(drive_subscribers), ", ".join(names) or "none")
+ )
+
+ for error in errors:
+ self.get_logger().error(error)
+ if errors:
+ return False
+
+ self.get_logger().info("preflight passed: navigation stack is ready for a goal")
+ return True
+
+ def _wait_for_tf(self, target_frame, source_frame):
+ tf_deadline = time.monotonic() + max(
+ 0.5, float(self.get_parameter("tf_wait_sec").value)
+ )
+ while rclpy.ok() and time.monotonic() < tf_deadline:
+ if self.tf_buffer.can_transform(
+ target_frame,
+ source_frame,
+ Time(),
+ timeout=Duration(seconds=0.2),
+ ):
+ return True
+ rclpy.spin_once(self, timeout_sec=0.05)
+ return False
+
+
+def main():
+ rclpy.init()
+ node = PreflightCheck()
+ success = False
+ try:
+ node.wait_for_inputs()
+ success = node.evaluate()
+ finally:
+ node.destroy_node()
+ rclpy.shutdown()
+ if not success:
+ sys.exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/src/planner/scripts/send_goal.py b/src/planner/scripts/send_goal.py
new file mode 100755
index 0000000..3859919
--- /dev/null
+++ b/src/planner/scripts/send_goal.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python3
+"""Publish one map-frame goal for the real-robot Hybrid A* stack."""
+
+import argparse
+import math
+import time
+
+import rclpy
+from geometry_msgs.msg import PoseStamped
+from rclpy.node import Node
+from rclpy.qos import DurabilityPolicy, QoSProfile, ReliabilityPolicy
+
+
+class GoalPublisher(Node):
+ def __init__(self, x, y, yaw, topic, frame_id):
+ super().__init__("send_goal")
+ goal_qos = QoSProfile(
+ depth=10,
+ reliability=ReliabilityPolicy.RELIABLE,
+ durability=DurabilityPolicy.TRANSIENT_LOCAL,
+ )
+ self.publisher = self.create_publisher(PoseStamped, topic, goal_qos)
+ self.goal = PoseStamped()
+ self.goal.header.frame_id = frame_id
+ self.goal.pose.position.x = x
+ self.goal.pose.position.y = y
+ self.goal.pose.orientation.z = math.sin(yaw * 0.5)
+ self.goal.pose.orientation.w = math.cos(yaw * 0.5)
+
+ def publish_once(self):
+ self.goal.header.stamp = self.get_clock().now().to_msg()
+ self.publisher.publish(self.goal)
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("x", type=float)
+ parser.add_argument("y", type=float)
+ parser.add_argument("yaw", type=float, nargs="?", default=0.0)
+ parser.add_argument("--topic", default="/goal_pose")
+ parser.add_argument("--frame", default="map")
+ parser.add_argument("--timeout", type=float, default=5.0)
+ parser.add_argument("--repeat", type=int, default=1)
+ args = parser.parse_args()
+
+ rclpy.init()
+ node = GoalPublisher(args.x, args.y, args.yaw, args.topic, args.frame)
+ deadline = time.monotonic() + max(0.5, args.timeout)
+ published = 0
+ while time.monotonic() < deadline:
+ rclpy.spin_once(node, timeout_sec=0.05)
+ if node.publisher.get_subscription_count() == 0:
+ continue
+ node.publish_once()
+ published += 1
+ time.sleep(0.10)
+ if published >= max(1, args.repeat):
+ break
+ if published == 0:
+ node.get_logger().error("no /goal_pose subscriber discovered before timeout")
+ node.destroy_node()
+ rclpy.shutdown()
+ return 1
+ node.get_logger().info(
+ f"published goal {published} times "
+ f"{args.frame}=({args.x:.3f}, {args.y:.3f}, {args.yaw:.3f} rad)"
+ )
+ node.destroy_node()
+ if rclpy.ok():
+ rclpy.shutdown()
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/src/planner/scripts/send_start_goal.py b/src/planner/scripts/send_start_goal.py
new file mode 100755
index 0000000..e129785
--- /dev/null
+++ b/src/planner/scripts/send_start_goal.py
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+"""Publish a known current pose and then a navigation goal."""
+
+import argparse
+import math
+import time
+
+import rclpy
+from geometry_msgs.msg import PoseStamped, PoseWithCovarianceStamped
+from rclpy.node import Node
+from rclpy.qos import DurabilityPolicy, QoSProfile, ReliabilityPolicy
+
+
+def fill_yaw_quaternion(q, yaw):
+ q.x = 0.0
+ q.y = 0.0
+ q.z = math.sin(0.5 * yaw)
+ q.w = math.cos(0.5 * yaw)
+
+
+class StartGoalPublisher(Node):
+ def __init__(self, args):
+ super().__init__("send_start_goal")
+ qos = QoSProfile(
+ depth=10,
+ reliability=ReliabilityPolicy.RELIABLE,
+ durability=DurabilityPolicy.TRANSIENT_LOCAL,
+ )
+ self.start_pub = self.create_publisher(
+ PoseWithCovarianceStamped, args.initialpose_topic, qos
+ )
+ self.goal_pub = self.create_publisher(PoseStamped, args.goal_topic, qos)
+ self.frame = args.frame
+
+ self.start = PoseWithCovarianceStamped()
+ self.start.header.frame_id = args.frame
+ self.start.pose.pose.position.x = args.start_x
+ self.start.pose.pose.position.y = args.start_y
+ fill_yaw_quaternion(self.start.pose.pose.orientation, args.start_yaw)
+ self.start.pose.covariance[0] = args.xy_covariance
+ self.start.pose.covariance[7] = args.xy_covariance
+ self.start.pose.covariance[35] = args.yaw_covariance
+
+ self.goal = PoseStamped()
+ self.goal.header.frame_id = args.frame
+ self.goal.pose.position.x = args.goal_x
+ self.goal.pose.position.y = args.goal_y
+ fill_yaw_quaternion(self.goal.pose.orientation, args.goal_yaw)
+
+ def publish_start(self):
+ self.start.header.stamp = self.get_clock().now().to_msg()
+ self.start_pub.publish(self.start)
+
+ def publish_goal(self):
+ self.goal.header.stamp = self.get_clock().now().to_msg()
+ self.goal_pub.publish(self.goal)
+
+
+def wait_for_subscribers(node, timeout):
+ deadline = time.monotonic() + max(0.5, timeout)
+ while time.monotonic() < deadline:
+ rclpy.spin_once(node, timeout_sec=0.05)
+ if (
+ node.start_pub.get_subscription_count() > 0
+ and node.goal_pub.get_subscription_count() > 0
+ ):
+ return True
+ return False
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("start_x", type=float)
+ parser.add_argument("start_y", type=float)
+ parser.add_argument("start_yaw", type=float)
+ parser.add_argument("goal_x", type=float)
+ parser.add_argument("goal_y", type=float)
+ parser.add_argument("goal_yaw", type=float, nargs="?", default=0.0)
+ parser.add_argument("--frame", default="map")
+ parser.add_argument("--initialpose-topic", default="/initialpose")
+ parser.add_argument("--goal-topic", default="/goal_pose")
+ parser.add_argument("--timeout", type=float, default=5.0)
+ parser.add_argument("--repeat", type=int, default=5)
+ parser.add_argument("--interval", type=float, default=0.10)
+ parser.add_argument("--goal-delay", type=float, default=0.30)
+ parser.add_argument("--xy-covariance", type=float, default=0.0025)
+ parser.add_argument("--yaw-covariance", type=float, default=0.0076)
+ args = parser.parse_args()
+
+ rclpy.init()
+ node = StartGoalPublisher(args)
+ try:
+ if not wait_for_subscribers(node, args.timeout):
+ node.get_logger().error(
+ "missing subscribers: /initialpose=%d /goal_pose=%d"
+ % (
+ node.start_pub.get_subscription_count(),
+ node.goal_pub.get_subscription_count(),
+ )
+ )
+ return 1
+
+ for _ in range(max(1, args.repeat)):
+ node.publish_start()
+ rclpy.spin_once(node, timeout_sec=0.02)
+ time.sleep(max(0.02, args.interval))
+
+ time.sleep(max(0.0, args.goal_delay))
+ for _ in range(max(1, args.repeat)):
+ node.publish_goal()
+ rclpy.spin_once(node, timeout_sec=0.02)
+ time.sleep(max(0.02, args.interval))
+
+ node.get_logger().info(
+ "published start %s=(%.3f, %.3f, %.3f) and goal %s=(%.3f, %.3f, %.3f)"
+ % (
+ args.frame,
+ args.start_x,
+ args.start_y,
+ args.start_yaw,
+ args.frame,
+ args.goal_x,
+ args.goal_y,
+ args.goal_yaw,
+ )
+ )
+ return 0
+ finally:
+ node.destroy_node()
+ if rclpy.ok():
+ rclpy.shutdown()
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/src/planner/scripts/topology_pure_pursuit_node.py b/src/planner/scripts/topology_pure_pursuit_node.py
old mode 100644
new mode 100755
index e2c430b..54086d6
--- a/src/planner/scripts/topology_pure_pursuit_node.py
+++ b/src/planner/scripts/topology_pure_pursuit_node.py
@@ -10,7 +10,7 @@ from geometry_msgs.msg import PoseStamped, Twist
from nav_msgs.msg import Odometry, Path as NavPath
from rclpy.node import Node
from rclpy.time import Time
-from std_msgs.msg import String
+from std_msgs.msg import Bool, String
import yaml
from rclpy.duration import Duration
@@ -53,6 +53,7 @@ class TopologyPurePursuit(Node):
self.declare_parameter("plan_topic", "/plan")
self.declare_parameter("cmd_vel_topic", "/cmd_vel")
self.declare_parameter("status_topic", "/topology_status")
+ self.declare_parameter("cancel_topic", "/navigation_cancel")
self.declare_parameter("enable_topology_planning", True)
self.declare_parameter("accept_external_plan", False)
self.declare_parameter("publish_cmd_vel", True)
@@ -69,11 +70,16 @@ class TopologyPurePursuit(Node):
self.declare_parameter("curvature_slowdown_gain", 0.18)
self.declare_parameter("max_angular_speed", 1.5)
self.declare_parameter("min_turning_radius", 0.35)
+ self.declare_parameter("goal_yaw_tolerance", 0.10)
+ self.declare_parameter("align_goal_yaw", True)
+ self.declare_parameter("final_alignment_offset", 0.25)
+ self.declare_parameter("final_alignment_speed", 0.10)
self.declare_parameter("latency_compensation", True)
self.declare_parameter("max_latency_compensation", 0.25)
self.declare_parameter("max_odom_age", 0.80)
self.declare_parameter("tf_timeout", 0.05)
self.declare_parameter("stop_without_plan", True)
+ self.declare_parameter("state_log_period", 1.0)
self.map_frame = self.get_parameter("map_frame").value
self.enable_topology_planning = bool(
@@ -107,6 +113,16 @@ class TopologyPurePursuit(Node):
self.min_turning_radius = float(
self.get_parameter("min_turning_radius").value
)
+ self.goal_yaw_tolerance = float(
+ self.get_parameter("goal_yaw_tolerance").value
+ )
+ self.align_goal_yaw = bool(self.get_parameter("align_goal_yaw").value)
+ self.final_alignment_offset = max(
+ 0.05, float(self.get_parameter("final_alignment_offset").value)
+ )
+ self.final_alignment_speed = max(
+ self.min_linear_speed, float(self.get_parameter("final_alignment_speed").value)
+ )
self.latency_compensation = bool(
self.get_parameter("latency_compensation").value
)
@@ -116,6 +132,9 @@ class TopologyPurePursuit(Node):
self.max_odom_age = float(self.get_parameter("max_odom_age").value)
self.tf_timeout = float(self.get_parameter("tf_timeout").value)
self.stop_without_plan = bool(self.get_parameter("stop_without_plan").value)
+ self.state_log_period = max(
+ 0.0, float(self.get_parameter("state_log_period").value)
+ )
self.nodes = {}
self.edges = {}
@@ -139,6 +158,9 @@ class TopologyPurePursuit(Node):
self.completed = True
self.tried_default_goal = False
self.last_tf_warn_ns = 0
+ self.last_state_log_ns = 0
+ self.last_cmd_linear = 0.0
+ self.last_cmd_angular = 0.0
self.plan_pub = self.create_publisher(
NavPath, self.get_parameter("plan_topic").value, 10
@@ -156,6 +178,12 @@ class TopologyPurePursuit(Node):
self._odom_callback,
10,
)
+ self.cancel_sub = self.create_subscription(
+ Bool,
+ self.get_parameter("cancel_topic").value,
+ self._cancel_callback,
+ 10,
+ )
self.goal_pose_sub = None
self.goal_node_sub = None
self.external_plan_sub = None
@@ -183,7 +211,9 @@ class TopologyPurePursuit(Node):
period = 1.0 / max(1.0, float(self.get_parameter("control_rate").value))
self.timer = self.create_timer(period, self._control_loop)
self._publish_status(
- f"ready graph_nodes={len(self.nodes)} graph_edges={sum(len(v) for v in self.edges.values())}"
+ f"ready graph_nodes={len(self.nodes)} "
+ f"graph_edges={sum(len(v) for v in self.edges.values())} "
+ f"motion_enabled={self.publish_cmd_vel}"
)
def _load_graph(self, graph_file_value):
@@ -295,7 +325,23 @@ class TopologyPurePursuit(Node):
self.active_goal_node = "external_plan"
self.current_path_index = 0
self.completed = False
- self._publish_status(f"external_plan_ok points={len(self.active_path)}")
+ path_length = self._path_length(self.active_path)
+ goal_x, goal_y, goal_yaw = self.active_path[-1]
+ self._publish_status(
+ f"external_plan_ok points={len(self.active_path)} "
+ f"length={path_length:.3f} goal=({goal_x:.3f},{goal_y:.3f},{goal_yaw:.3f})"
+ )
+
+ def _cancel_callback(self, msg):
+ if not msg.data:
+ return
+ self.active_path = []
+ self.active_node_path = []
+ self.active_goal_node = ""
+ self.current_path_index = 0
+ self.completed = True
+ self._publish_cmd(0.0, 0.0)
+ self._publish_status("navigation_cancelled")
def plan_to_node(self, goal_node):
if goal_node not in self.nodes:
@@ -496,31 +542,57 @@ class TopologyPurePursuit(Node):
def _control_loop(self):
if not self.publish_cmd_vel:
+ pose = self._current_pose(compensate=False)
+ state_args = {"pose": pose, "mode": "motion_disabled"}
+ if pose is not None and self.active_path:
+ x, y, yaw, _, _ = pose
+ goal_x, goal_y, goal_yaw = self.active_path[-1]
+ state_args["goal_distance"] = math.hypot(goal_x - x, goal_y - y)
+ state_args["goal_yaw_error"] = abs(normalize_angle(goal_yaw - yaw))
+ nearest_index = self._nearest_path_index(x, y)
+ nearest_x, nearest_y, _ = self.active_path[nearest_index]
+ state_args["nearest_index"] = nearest_index
+ state_args["cross_track_error"] = math.hypot(nearest_x - x, nearest_y - y)
+ self._maybe_log_state(**state_args)
return
pose = self._current_pose(compensate=True)
if pose is None:
self._stop_if_needed("stop no_recent_odom")
+ self._maybe_log_state(pose=None, mode="no_recent_odom")
return
if self.completed or len(self.active_path) < 2:
if self.stop_without_plan:
self._publish_cmd(0.0, 0.0)
+ self._maybe_log_state(pose=pose, mode="idle")
return
x, y, yaw, _, _ = pose
goal_x, goal_y, _ = self.active_path[-1]
+ goal_yaw = self.active_path[-1][2]
goal_distance = math.hypot(goal_x - x, goal_y - y)
+ goal_yaw_error = abs(normalize_angle(goal_yaw - yaw))
if goal_distance <= self.goal_tolerance:
+ if self.align_goal_yaw and goal_yaw_error > self.goal_yaw_tolerance:
+ self._align_final_yaw(
+ x, y, yaw, goal_x, goal_y, goal_yaw, goal_yaw_error
+ )
+ return
self.completed = True
self._publish_cmd(0.0, 0.0)
- self._publish_status(f"goal_reached {self.active_goal_node}")
+ self._publish_status(
+ f"goal_reached {self.active_goal_node} "
+ f"goal_error={goal_distance:.3f} yaw_error={goal_yaw_error:.3f}"
+ )
return
nearest_index = self._nearest_path_index(x, y)
self.current_path_index = nearest_index
target_index = self._lookahead_index(x, y, nearest_index)
target_x, target_y, target_path_yaw = self.active_path[target_index]
+ nearest_x, nearest_y, _ = self.active_path[nearest_index]
+ cross_track_error = math.hypot(nearest_x - x, nearest_y - y)
dx = target_x - x
dy = target_y - y
@@ -540,6 +612,51 @@ class TopologyPurePursuit(Node):
angular = speed * curvature
angular = max(-self.max_angular_speed, min(self.max_angular_speed, angular))
self._publish_cmd(speed, angular)
+ self._maybe_log_state(
+ pose=pose,
+ mode="tracking",
+ goal_distance=goal_distance,
+ goal_yaw_error=goal_yaw_error,
+ nearest_index=nearest_index,
+ target_index=target_index,
+ cross_track_error=cross_track_error,
+ reverse=reverse,
+ curvature=curvature,
+ lookahead=lookahead,
+ )
+
+ def _align_final_yaw(self, x, y, yaw, goal_x, goal_y, goal_yaw, goal_yaw_error):
+ reverse = math.cos(normalize_angle(goal_yaw - yaw)) < 0.0
+ target_x = goal_x - self.final_alignment_offset * math.cos(goal_yaw) if reverse else goal_x + self.final_alignment_offset * math.cos(goal_yaw)
+ target_y = goal_y - self.final_alignment_offset * math.sin(goal_yaw) if reverse else goal_y + self.final_alignment_offset * math.sin(goal_yaw)
+ control_yaw = normalize_angle(yaw + math.pi) if reverse else yaw
+ dx = target_x - x
+ dy = target_y - y
+ local_x = math.cos(control_yaw) * dx + math.sin(control_yaw) * dy
+ local_y = -math.sin(control_yaw) * dx + math.cos(control_yaw) * dy
+ lookahead = max(0.05, math.hypot(local_x, local_y))
+ curvature = 2.0 * local_y / (lookahead * lookahead)
+ if self.min_turning_radius > 1e-3:
+ max_curvature = 1.0 / self.min_turning_radius
+ curvature = max(-max_curvature, min(max_curvature, curvature))
+ speed = -self.final_alignment_speed if reverse else self.final_alignment_speed
+ angular = speed * curvature
+ angular = max(-self.max_angular_speed, min(self.max_angular_speed, angular))
+ self._publish_cmd(speed, angular)
+ self._maybe_log_state(
+ pose=(x, y, yaw, 0.0, 0.0),
+ mode="align_goal_yaw",
+ goal_distance=math.hypot(goal_x - x, goal_y - y),
+ goal_yaw_error=goal_yaw_error,
+ nearest_index=self.current_path_index,
+ target_index=len(self.active_path) - 1 if self.active_path else 0,
+ reverse=reverse,
+ curvature=curvature,
+ lookahead=lookahead,
+ )
+ self._publish_status(
+ f"aligning_goal_yaw error={goal_yaw_error:.3f} reverse={reverse}"
+ )
def _nearest_path_index(self, x, y):
start = max(0, self.current_path_index - 5)
@@ -583,6 +700,62 @@ class TopologyPurePursuit(Node):
return fallback_yaw
return math.atan2(ty - sy, tx - sx)
+ @staticmethod
+ def _path_length(path):
+ return sum(
+ math.hypot(b[0] - a[0], b[1] - a[1])
+ for a, b in zip(path, path[1:])
+ )
+
+ def _maybe_log_state(
+ self,
+ pose=None,
+ mode="idle",
+ goal_distance=None,
+ goal_yaw_error=None,
+ nearest_index=None,
+ target_index=None,
+ cross_track_error=None,
+ reverse=False,
+ curvature=None,
+ lookahead=None,
+ ):
+ if self.state_log_period <= 0.0:
+ return
+ now_ns = self.get_clock().now().nanoseconds
+ min_period_ns = int(self.state_log_period * 1_000_000_000)
+ if now_ns - self.last_state_log_ns < min_period_ns:
+ return
+ self.last_state_log_ns = now_ns
+
+ parts = [f"tracker_state mode={mode}"]
+ if pose is None:
+ parts.append("pose=unavailable")
+ else:
+ x, y, yaw, vx, wz = pose
+ parts.append(f"pose=({x:.3f},{y:.3f},{yaw:.3f})")
+ parts.append(f"odom_twist=({vx:.3f},{wz:.3f})")
+
+ parts.append(f"path_points={len(self.active_path)}")
+ parts.append(f"completed={self.completed}")
+ if goal_distance is not None:
+ parts.append(f"goal_distance={goal_distance:.3f}")
+ if goal_yaw_error is not None:
+ parts.append(f"yaw_error={goal_yaw_error:.3f}")
+ if nearest_index is not None:
+ parts.append(f"nearest_index={nearest_index}")
+ if target_index is not None:
+ parts.append(f"target_index={target_index}")
+ if cross_track_error is not None:
+ parts.append(f"cross_track={cross_track_error:.3f}")
+ if curvature is not None:
+ parts.append(f"curvature={curvature:.3f}")
+ if lookahead is not None:
+ parts.append(f"lookahead={lookahead:.3f}")
+ parts.append(f"reverse={reverse}")
+ parts.append(f"cmd=({self.last_cmd_linear:.3f},{self.last_cmd_angular:.3f})")
+ self.get_logger().info(" ".join(parts))
+
def _target_speed(self, abs_curvature, goal_distance, reverse):
base_speed = self.reverse_speed if reverse else self.linear_speed
speed = base_speed
@@ -594,9 +767,11 @@ class TopologyPurePursuit(Node):
return -speed if reverse else speed
def _publish_cmd(self, linear, angular):
+ self.last_cmd_linear = float(linear)
+ self.last_cmd_angular = float(angular)
cmd = Twist()
- cmd.linear.x = float(linear)
- cmd.angular.z = float(angular)
+ cmd.linear.x = self.last_cmd_linear
+ cmd.angular.z = self.last_cmd_angular
self.cmd_pub.publish(cmd)
def _stop_if_needed(self, reason):
diff --git a/src/planner/src/grid_astar_theta_node.cpp b/src/planner/src/grid_astar_theta_node.cpp
index 3b522ab..61bf079 100644
--- a/src/planner/src/grid_astar_theta_node.cpp
+++ b/src/planner/src/grid_astar_theta_node.cpp
@@ -1,11 +1,1321 @@
-#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
+#include
-int main()
+#include "builtin_interfaces/msg/time.hpp"
+#include "geometry_msgs/msg/pose_stamped.hpp"
+#include "nav_msgs/msg/occupancy_grid.hpp"
+#include "nav_msgs/msg/odometry.hpp"
+#include "nav_msgs/msg/path.hpp"
+#include "rclcpp/rclcpp.hpp"
+#include "std_msgs/msg/bool.hpp"
+#include "std_msgs/msg/float32_multi_array.hpp"
+#include "std_msgs/msg/string.hpp"
+#include "tf2/exceptions.h"
+#include "tf2_geometry_msgs/tf2_geometry_msgs.hpp"
+#include "tf2_ros/buffer.h"
+#include "tf2_ros/transform_listener.h"
+
+namespace
{
- std::cout << "planner package: Nav2 minimal global planning stack" << std::endl;
- std::cout << " - slam_toolbox (online_async)" << std::endl;
- std::cout << " - planner_server (SmacPlannerHybrid)" << std::endl;
- std::cout << " - global_costmap + local_costmap" << std::endl;
- std::cout << " - lifecycle_manager" << std::endl;
+constexpr double kPi = 3.14159265358979323846;
+
+double yawFromQuaternion(const geometry_msgs::msg::Quaternion & q)
+{
+ return std::atan2(
+ 2.0 * (q.w * q.z + q.x * q.y),
+ 1.0 - 2.0 * (q.y * q.y + q.z * q.z));
+}
+
+double normalizeAngle(double angle)
+{
+ return std::atan2(std::sin(angle), std::cos(angle));
+}
+
+double positiveAngle(double angle)
+{
+ const double wrapped = normalizeAngle(angle);
+ return wrapped < 0.0 ? wrapped + 2.0 * kPi : wrapped;
+}
+
+builtin_interfaces::msg::Time toMsgTime(const rclcpp::Time & time)
+{
+ builtin_interfaces::msg::Time stamp;
+ const int64_t nanoseconds = time.nanoseconds();
+ stamp.sec = static_cast(nanoseconds / 1000000000LL);
+ stamp.nanosec = static_cast(nanoseconds % 1000000000LL);
+ return stamp;
+}
+
+geometry_msgs::msg::PoseStamped makePose(
+ const std::string & frame_id,
+ const builtin_interfaces::msg::Time & stamp,
+ double x,
+ double y,
+ double yaw)
+{
+ geometry_msgs::msg::PoseStamped pose;
+ pose.header.frame_id = frame_id;
+ pose.header.stamp = stamp;
+ pose.pose.position.x = x;
+ pose.pose.position.y = y;
+ pose.pose.position.z = 0.0;
+ pose.pose.orientation.z = std::sin(0.5 * yaw);
+ pose.pose.orientation.w = std::cos(0.5 * yaw);
+ return pose;
+}
+} // namespace
+
+class GridAstarThetaPlanner : public rclcpp::Node
+{
+public:
+ GridAstarThetaPlanner()
+ : Node("grid_astar_theta_planner"),
+ tf_buffer_(this->get_clock()),
+ tf_listener_(tf_buffer_)
+ {
+ declareParameters();
+ loadParameters();
+ validateMap();
+
+ plan_pub_ = create_publisher(plan_topic_, 10);
+ status_pub_ = create_publisher(status_topic_, 10);
+
+ odom_sub_ = create_subscription(
+ odom_topic_, 10,
+ std::bind(&GridAstarThetaPlanner::odomCallback, this, std::placeholders::_1));
+ goal_sub_ = create_subscription(
+ goal_pose_topic_, 10,
+ std::bind(&GridAstarThetaPlanner::goalCallback, this, std::placeholders::_1));
+ obstacles_sub_ = create_subscription(
+ obstacles_topic_, 10,
+ std::bind(&GridAstarThetaPlanner::obstaclesCallback, this, std::placeholders::_1));
+ clear_obstacles_sub_ = create_subscription(
+ clear_obstacles_topic_, 10,
+ std::bind(&GridAstarThetaPlanner::clearObstaclesCallback, this, std::placeholders::_1));
+ cancel_sub_ = create_subscription(
+ cancel_topic_, 10,
+ std::bind(&GridAstarThetaPlanner::cancelCallback, this, std::placeholders::_1));
+ const auto static_map_qos = rclcpp::QoS(rclcpp::KeepLast(1))
+ .reliable().transient_local();
+ costmap_sub_ = create_subscription(
+ costmap_topic_, static_map_qos,
+ std::bind(&GridAstarThetaPlanner::costmapCallback, this, std::placeholders::_1));
+
+ const auto period = std::chrono::duration_cast(
+ std::chrono::duration(1.0 / std::max(0.1, replan_rate_)));
+ replan_timer_ = create_wall_timer(period, std::bind(&GridAstarThetaPlanner::onReplanTimer, this));
+ if (state_log_period_ > 0.0) {
+ const auto log_period = std::chrono::duration_cast(
+ std::chrono::duration(std::max(0.001, state_log_period_)));
+ state_log_timer_ =
+ create_wall_timer(log_period, std::bind(&GridAstarThetaPlanner::logRuntimeState, this));
+ }
+
+ publishStatus(
+ "ready hybrid_astar size=" + std::to_string(width_) + "x" + std::to_string(height_) +
+ " resolution=" + std::to_string(resolution_) +
+ " min_turning_radius=" + std::to_string(min_turning_radius_));
+ }
+
+private:
+ struct Pose2D
+ {
+ double x{0.0};
+ double y{0.0};
+ double yaw{0.0};
+ };
+
+ struct Obstacle
+ {
+ double x{0.0};
+ double y{0.0};
+ double radius{0.0};
+ };
+
+ struct Cell
+ {
+ int x{0};
+ int y{0};
+ };
+
+ struct SearchState
+ {
+ int x{0};
+ int y{0};
+ int yaw_bin{0};
+ int gear{1};
+ };
+
+ struct SearchNode
+ {
+ SearchState state;
+ double x{0.0};
+ double y{0.0};
+ double yaw{0.0};
+ double g{0.0};
+ int parent{-1};
+ int gear{1};
+ int steering{0};
+ std::vector segment;
+ };
+
+ struct QueueItem
+ {
+ double priority{0.0};
+ int node_index{0};
+
+ bool operator<(const QueueItem & other) const
+ {
+ return priority > other.priority;
+ }
+ };
+
+ struct GridQueueItem
+ {
+ double cost{0.0};
+ int cell_index{0};
+
+ bool operator<(const GridQueueItem & other) const
+ {
+ return cost > other.cost;
+ }
+ };
+
+ struct CostmapView
+ {
+ nav_msgs::msg::OccupancyGrid::SharedPtr msg;
+ bool enabled{false};
+ bool same_frame{true};
+ double frame_tx{0.0};
+ double frame_ty{0.0};
+ double frame_yaw{0.0};
+ double origin_yaw{0.0};
+ };
+
+ struct SearchResult
+ {
+ std::vector path;
+ int iterations{0};
+ bool timed_out{false};
+ bool reached{false};
+ };
+
+ void declareParameters()
+ {
+ declare_parameter("map_frame", "map");
+ declare_parameter("odom_topic", "/odom");
+ declare_parameter("goal_pose_topic", "/goal_pose");
+ declare_parameter("obstacles_topic", "/random_obstacles");
+ declare_parameter("clear_obstacles_topic", "/clear_random_obstacles");
+ declare_parameter("cancel_topic", "/navigation_cancel");
+ declare_parameter("costmap_topic", "/global_costmap/costmap");
+ declare_parameter("plan_topic", "/plan");
+ declare_parameter("status_topic", "/ackermann_lattice_status");
+ declare_parameter("map_min_x", -5.0);
+ declare_parameter("map_min_y", -5.0);
+ declare_parameter("map_max_x", 5.0);
+ declare_parameter("map_max_y", 5.0);
+ declare_parameter("resolution", 0.10);
+ declare_parameter("fit_map_to_costmap", true);
+ declare_parameter("obstacle_format", "xyr");
+ declare_parameter("default_obstacle_radius", 0.15);
+ declare_parameter("robot_radius", 0.14);
+ declare_parameter("localization_error", 0.10);
+ declare_parameter("latency_margin", 0.05);
+ declare_parameter("safety_margin", 0.05);
+ declare_parameter("unknown_obstacle_radius", 0.15);
+ declare_parameter("accumulate_obstacles", true);
+ declare_parameter("obstacle_merge_distance", 0.20);
+ declare_parameter("costmap_occupied_threshold", 50);
+ declare_parameter("unknown_is_obstacle", true);
+ declare_parameter("outside_costmap_is_obstacle", true);
+ declare_parameter("inflate_static_map", false);
+ declare_parameter("plan_on_obstacle_update", true);
+ declare_parameter("max_iterations", 50000);
+ declare_parameter("planning_timeout_ms", 100.0);
+ declare_parameter("replan_rate", 2.0);
+ declare_parameter("min_turning_radius", 0.40);
+ declare_parameter("yaw_bins", 72);
+ declare_parameter("primitive_length", 0.12);
+ declare_parameter("collision_sample_step", 0.03);
+ declare_parameter("goal_xy_tolerance", 0.20);
+ declare_parameter("goal_yaw_tolerance", 3.0 * kPi / 180.0);
+ declare_parameter("require_goal_yaw", true);
+ declare_parameter("use_grid_heuristic", true);
+ declare_parameter("heuristic_weight", 1.0);
+ declare_parameter("goal_yaw_heuristic_weight", 1.0);
+ declare_parameter("reverse_penalty", 1.3);
+ declare_parameter("turn_penalty", 0.05);
+ declare_parameter("gear_change_penalty", 0.20);
+ declare_parameter("steering_change_penalty", 0.02);
+ declare_parameter("max_odom_age", 0.80);
+ declare_parameter("tf_timeout", 0.05);
+ declare_parameter("state_log_period", 1.0);
+ declare_parameter("search_progress_log_period_ms", 500.0);
+ }
+
+ void loadParameters()
+ {
+ map_frame_ = get_parameter("map_frame").as_string();
+ odom_topic_ = get_parameter("odom_topic").as_string();
+ goal_pose_topic_ = get_parameter("goal_pose_topic").as_string();
+ obstacles_topic_ = get_parameter("obstacles_topic").as_string();
+ clear_obstacles_topic_ = get_parameter("clear_obstacles_topic").as_string();
+ cancel_topic_ = get_parameter("cancel_topic").as_string();
+ costmap_topic_ = get_parameter("costmap_topic").as_string();
+ plan_topic_ = get_parameter("plan_topic").as_string();
+ status_topic_ = get_parameter("status_topic").as_string();
+ map_min_x_ = get_parameter("map_min_x").as_double();
+ map_min_y_ = get_parameter("map_min_y").as_double();
+ map_max_x_ = get_parameter("map_max_x").as_double();
+ map_max_y_ = get_parameter("map_max_y").as_double();
+ resolution_ = get_parameter("resolution").as_double();
+ fit_map_to_costmap_ = get_parameter("fit_map_to_costmap").as_bool();
+ obstacle_format_ = get_parameter("obstacle_format").as_string();
+ default_obstacle_radius_ = get_parameter("default_obstacle_radius").as_double();
+ robot_radius_ = get_parameter("robot_radius").as_double();
+ localization_error_ = get_parameter("localization_error").as_double();
+ latency_margin_ = get_parameter("latency_margin").as_double();
+ safety_margin_ = get_parameter("safety_margin").as_double();
+ unknown_obstacle_radius_ = get_parameter("unknown_obstacle_radius").as_double();
+ accumulate_obstacles_ = get_parameter("accumulate_obstacles").as_bool();
+ obstacle_merge_distance_ = get_parameter("obstacle_merge_distance").as_double();
+ costmap_occupied_threshold_ =
+ static_cast(get_parameter("costmap_occupied_threshold").as_int());
+ unknown_is_obstacle_ = get_parameter("unknown_is_obstacle").as_bool();
+ outside_costmap_is_obstacle_ = get_parameter("outside_costmap_is_obstacle").as_bool();
+ inflate_static_map_ = get_parameter("inflate_static_map").as_bool();
+ plan_on_obstacle_update_ = get_parameter("plan_on_obstacle_update").as_bool();
+ max_iterations_ = static_cast(get_parameter("max_iterations").as_int());
+ planning_timeout_ms_ = get_parameter("planning_timeout_ms").as_double();
+ replan_rate_ = get_parameter("replan_rate").as_double();
+ min_turning_radius_ = get_parameter("min_turning_radius").as_double();
+ yaw_bins_ = static_cast(get_parameter("yaw_bins").as_int());
+ primitive_length_ = get_parameter("primitive_length").as_double();
+ collision_sample_step_ = get_parameter("collision_sample_step").as_double();
+ goal_xy_tolerance_ = get_parameter("goal_xy_tolerance").as_double();
+ goal_yaw_tolerance_ = get_parameter("goal_yaw_tolerance").as_double();
+ require_goal_yaw_ = get_parameter("require_goal_yaw").as_bool();
+ use_grid_heuristic_ = get_parameter("use_grid_heuristic").as_bool();
+ heuristic_weight_ = get_parameter("heuristic_weight").as_double();
+ goal_yaw_heuristic_weight_ = get_parameter("goal_yaw_heuristic_weight").as_double();
+ reverse_penalty_ = get_parameter("reverse_penalty").as_double();
+ turn_penalty_ = get_parameter("turn_penalty").as_double();
+ gear_change_penalty_ = get_parameter("gear_change_penalty").as_double();
+ steering_change_penalty_ = get_parameter("steering_change_penalty").as_double();
+ max_odom_age_ = get_parameter("max_odom_age").as_double();
+ tf_timeout_ = get_parameter("tf_timeout").as_double();
+ state_log_period_ = get_parameter("state_log_period").as_double();
+ search_progress_log_period_ms_ = get_parameter("search_progress_log_period_ms").as_double();
+ }
+
+ void validateMap()
+ {
+ if (resolution_ <= 0.0) {
+ throw std::runtime_error("resolution must be positive");
+ }
+ if (map_max_x_ <= map_min_x_ || map_max_y_ <= map_min_y_) {
+ throw std::runtime_error("invalid map bounds");
+ }
+ if (min_turning_radius_ <= 0.0) {
+ throw std::runtime_error("min_turning_radius must be positive");
+ }
+ if (yaw_bins_ < 8) {
+ throw std::runtime_error("yaw_bins must be >= 8");
+ }
+ if (primitive_length_ <= 0.0 || collision_sample_step_ <= 0.0) {
+ throw std::runtime_error("primitive_length and collision_sample_step must be positive");
+ }
+ if (heuristic_weight_ <= 0.0) {
+ throw std::runtime_error("heuristic_weight must be positive");
+ }
+ if (goal_yaw_heuristic_weight_ < 0.0) {
+ throw std::runtime_error("goal_yaw_heuristic_weight must be non-negative");
+ }
+ if (goal_yaw_tolerance_ < 0.0) {
+ throw std::runtime_error("goal_yaw_tolerance must be non-negative");
+ }
+
+ width_ = static_cast(std::ceil((map_max_x_ - map_min_x_) / resolution_));
+ height_ = static_cast(std::ceil((map_max_y_ - map_min_y_) / resolution_));
+ if (width_ <= 1 || height_ <= 1) {
+ throw std::runtime_error("map bounds are too small for the selected resolution");
+ }
+ }
+
+ void odomCallback(const nav_msgs::msg::Odometry::SharedPtr msg)
+ {
+ latest_odom_ = msg;
+ }
+
+ void goalCallback(const geometry_msgs::msg::PoseStamped::SharedPtr msg)
+ {
+ Pose2D transformed;
+ if (!transformPoseMsg(*msg, transformed)) {
+ publishStatus("goal_rejected transform_failed");
+ return;
+ }
+ goal_pose_ = transformed;
+ has_goal_ = true;
+ pending_replan_ = false;
+ publishStatus(
+ "goal_received map=(" + formatDouble(goal_pose_.x) + "," +
+ formatDouble(goal_pose_.y) + "," + formatDouble(goal_pose_.yaw) + ")");
+ plan();
+ }
+
+ void obstaclesCallback(const std_msgs::msg::Float32MultiArray::SharedPtr msg)
+ {
+ const auto incoming = parseObstacles(msg->data);
+ if (accumulate_obstacles_) {
+ mergeObstacles(incoming);
+ } else {
+ obstacles_ = incoming;
+ }
+ publishStatus(
+ "obstacles_updated incoming=" + std::to_string(incoming.size()) +
+ " stored=" + std::to_string(obstacles_.size()));
+ requestReplan();
+ }
+
+ void clearObstaclesCallback(const std_msgs::msg::Bool::SharedPtr msg)
+ {
+ if (!msg->data) {
+ return;
+ }
+ obstacles_.clear();
+ publishStatus("obstacles_cleared");
+ requestReplan();
+ }
+
+ void cancelCallback(const std_msgs::msg::Bool::SharedPtr msg)
+ {
+ if (!msg->data) {
+ return;
+ }
+ has_goal_ = false;
+ pending_replan_ = false;
+ last_path_.clear();
+ publishEmptyPath();
+ publishStatus("navigation_cancelled");
+ }
+
+ void costmapCallback(const nav_msgs::msg::OccupancyGrid::SharedPtr msg)
+ {
+ latest_costmap_ = msg;
+ maybeFitMapToCostmap(*msg);
+ requestReplan();
+ }
+
+ void onReplanTimer()
+ {
+ if (!pending_replan_ || !has_goal_) {
+ return;
+ }
+ pending_replan_ = false;
+ plan();
+ }
+
+ void requestReplan()
+ {
+ if (plan_on_obstacle_update_ && has_goal_) {
+ pending_replan_ = true;
+ }
+ }
+
+ void maybeFitMapToCostmap(const nav_msgs::msg::OccupancyGrid & msg)
+ {
+ if (!fit_map_to_costmap_ || msg.info.width == 0 || msg.info.height == 0 ||
+ msg.info.resolution <= 0.0)
+ {
+ return;
+ }
+
+ const std::string frame = msg.header.frame_id.empty() ? map_frame_ : msg.header.frame_id;
+ const double origin_yaw = yawFromQuaternion(msg.info.origin.orientation);
+ if (frame != map_frame_ || std::abs(normalizeAngle(origin_yaw)) > 1e-3) {
+ return;
+ }
+
+ const double new_min_x = msg.info.origin.position.x;
+ const double new_min_y = msg.info.origin.position.y;
+ const double new_max_x = new_min_x + static_cast(msg.info.width) * msg.info.resolution;
+ const double new_max_y = new_min_y + static_cast(msg.info.height) * msg.info.resolution;
+ if (std::abs(new_min_x - map_min_x_) < 1e-6 &&
+ std::abs(new_min_y - map_min_y_) < 1e-6 &&
+ std::abs(new_max_x - map_max_x_) < 1e-6 &&
+ std::abs(new_max_y - map_max_y_) < 1e-6)
+ {
+ return;
+ }
+
+ map_min_x_ = new_min_x;
+ map_min_y_ = new_min_y;
+ map_max_x_ = new_max_x;
+ map_max_y_ = new_max_y;
+ validateMap();
+ publishStatus(
+ "costmap_bounds_applied size=" + std::to_string(width_) + "x" + std::to_string(height_));
+ }
+
+ std::vector parseObstacles(const std::vector & values) const
+ {
+ const int stride = obstacle_format_ == "xyr" ? 3 : 2;
+ std::vector obstacles;
+ const int count = static_cast(values.size()) / stride;
+ obstacles.reserve(count);
+ for (int i = 0; i < count; ++i) {
+ const int base = i * stride;
+ Obstacle obs;
+ obs.x = values[base];
+ obs.y = values[base + 1];
+ obs.radius = default_obstacle_radius_;
+ if (stride == 3) {
+ obs.radius = std::max(0.0, static_cast(values[base + 2]));
+ }
+ obstacles.push_back(obs);
+ }
+ return obstacles;
+ }
+
+ void mergeObstacles(const std::vector & incoming)
+ {
+ const double merge_distance = std::max(0.0, obstacle_merge_distance_);
+ const double merge_distance_sq = merge_distance * merge_distance;
+
+ for (const auto & obs : incoming) {
+ auto matched = std::find_if(
+ obstacles_.begin(), obstacles_.end(),
+ [&obs, merge_distance_sq](const Obstacle & existing) {
+ const double dx = obs.x - existing.x;
+ const double dy = obs.y - existing.y;
+ return dx * dx + dy * dy <= merge_distance_sq;
+ });
+
+ if (matched == obstacles_.end()) {
+ obstacles_.push_back(obs);
+ continue;
+ }
+
+ matched->x = obs.x;
+ matched->y = obs.y;
+ matched->radius = std::max(matched->radius, obs.radius);
+ }
+ }
+
+ bool plan()
+ {
+ const auto search_start = std::chrono::steady_clock::now();
+ Pose2D start_pose;
+ if (!currentPose(start_pose)) {
+ publishStatus("plan_rejected no_recent_odom");
+ return false;
+ }
+ if (!has_goal_) {
+ publishStatus("plan_rejected no_goal");
+ return false;
+ }
+
+ publishStatus(
+ "plan_start start=(" + formatDouble(start_pose.x) + "," + formatDouble(start_pose.y) +
+ "," + formatDouble(start_pose.yaw) + ") goal=(" + formatDouble(goal_pose_.x) + "," +
+ formatDouble(goal_pose_.y) + "," + formatDouble(goal_pose_.yaw) + ") obstacles=" +
+ std::to_string(obstacles_.size()) + " resolution=" + formatDouble(resolution_) +
+ " yaw_bins=" + std::to_string(yaw_bins_) + " timeout_ms=" +
+ formatDouble(planning_timeout_ms_) + " grid_heuristic=" +
+ std::string(use_grid_heuristic_ ? "true" : "false") + " heuristic_weight=" +
+ formatDouble(heuristic_weight_) + " yaw_heuristic_weight=" +
+ formatDouble(goal_yaw_heuristic_weight_) + " goal_yaw_tolerance=" +
+ formatDouble(goal_yaw_tolerance_));
+
+ Cell start_cell;
+ Cell goal_cell;
+ if (!worldToGrid(start_pose.x, start_pose.y, start_cell) ||
+ !worldToGrid(goal_pose_.x, goal_pose_.y, goal_cell))
+ {
+ publishEmptyPath();
+ publishStatus("plan_rejected out_of_bounds clear_path");
+ return false;
+ }
+
+ auto occupied = buildOccupancy(start_pose, goal_pose_);
+ if (!poseCollisionFree(start_pose, occupied) || !poseCollisionFree(goal_pose_, occupied)) {
+ return handlePlanningFailure("start_or_goal_occupied", occupied);
+ }
+
+ const SearchResult result = search(start_pose, goal_pose_, occupied, search_start);
+ if (!result.reached || result.path.empty()) {
+ return handlePlanningFailure(result.timed_out ? "timeout" : "no_path", occupied);
+ }
+
+ last_path_ = result.path;
+ publishPath(last_path_);
+
+ const Pose2D & published_end = result.path.back();
+ const double goal_error =
+ std::hypot(published_end.x - goal_pose_.x, published_end.y - goal_pose_.y);
+ const double yaw_error = std::abs(normalizeAngle(published_end.yaw - goal_pose_.yaw));
+ const double planning_ms = std::chrono::duration(
+ std::chrono::steady_clock::now() - search_start).count();
+ publishStatus(
+ "plan_ok hybrid_astar points=" + std::to_string(last_path_.size()) +
+ " iterations=" + std::to_string(result.iterations) +
+ " obstacles=" + std::to_string(obstacles_.size()) +
+ " planning_ms=" + formatDouble(planning_ms) +
+ " goal_error=" + formatDouble(goal_error) +
+ " yaw_error=" + formatDouble(yaw_error));
+ return true;
+ }
+
+ bool handlePlanningFailure(const std::string & reason, const std::vector & occupied)
+ {
+ if (!last_path_.empty() && pathCollisionFree(last_path_, occupied)) {
+ publishStatus("plan_failed " + reason + " keeping_last_path");
+ return false;
+ }
+
+ last_path_.clear();
+ publishEmptyPath();
+ publishStatus("plan_failed " + reason + " clear_path");
+ return false;
+ }
+
+ std::vector buildOccupancy(const Pose2D &, const Pose2D &)
+ {
+ std::vector occupied(static_cast(width_ * height_), false);
+ const CostmapView costmap = makeCostmapView();
+
+ if (costmap.enabled) {
+ for (int gy = 0; gy < height_; ++gy) {
+ for (int gx = 0; gx < width_; ++gx) {
+ const auto [wx, wy] = gridToWorld(Cell{gx, gy});
+ if (costmapOccupiedAt(costmap, wx, wy)) {
+ occupied[index(Cell{gx, gy})] = true;
+ }
+ }
+ }
+ if (inflate_static_map_) {
+ const double inflation_radius =
+ robot_radius_ + localization_error_ + latency_margin_ + safety_margin_;
+ const int cell_radius = static_cast(std::ceil(inflation_radius / resolution_));
+ const std::vector source = occupied;
+ for (int gy = 0; gy < height_; ++gy) {
+ for (int gx = 0; gx < width_; ++gx) {
+ if (!source[index(Cell{gx, gy})]) {
+ continue;
+ }
+ for (int oy = -cell_radius; oy <= cell_radius; ++oy) {
+ for (int ox = -cell_radius; ox <= cell_radius; ++ox) {
+ if (ox * ox + oy * oy > cell_radius * cell_radius) {
+ continue;
+ }
+ const Cell inflated{gx + ox, gy + oy};
+ if (inBounds(inflated)) {
+ occupied[index(inflated)] = true;
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+
+ const double base_padding =
+ robot_radius_ + localization_error_ + latency_margin_ + safety_margin_;
+ for (const auto & obs : obstacles_) {
+ const double radius = std::max(obs.radius, unknown_obstacle_radius_) + base_padding;
+ Cell min_cell;
+ Cell max_cell;
+ worldToGrid(obs.x - radius, obs.y - radius, min_cell, true);
+ worldToGrid(obs.x + radius, obs.y + radius, max_cell, true);
+ const double radius_sq = radius * radius;
+ for (int gy = min_cell.y; gy <= max_cell.y; ++gy) {
+ for (int gx = min_cell.x; gx <= max_cell.x; ++gx) {
+ const auto [wx, wy] = gridToWorld(Cell{gx, gy});
+ const double dx = wx - obs.x;
+ const double dy = wy - obs.y;
+ if ((dx * dx + dy * dy) <= radius_sq) {
+ occupied[index(Cell{gx, gy})] = true;
+ }
+ }
+ }
+ }
+
+ return occupied;
+ }
+
+ CostmapView makeCostmapView()
+ {
+ CostmapView view;
+ view.msg = latest_costmap_;
+ if (!view.msg || view.msg->info.width == 0 || view.msg->info.height == 0 ||
+ view.msg->info.resolution <= 0.0 || view.msg->data.empty())
+ {
+ return view;
+ }
+
+ const std::string frame =
+ view.msg->header.frame_id.empty() ? map_frame_ : view.msg->header.frame_id;
+ view.origin_yaw = yawFromQuaternion(view.msg->info.origin.orientation);
+ view.same_frame = frame == map_frame_;
+ if (!view.same_frame) {
+ try {
+ const auto transform = tf_buffer_.lookupTransform(
+ frame, map_frame_, tf2::TimePointZero,
+ tf2::durationFromSec(tf_timeout_));
+ const auto & t = transform.transform.translation;
+ view.frame_tx = t.x;
+ view.frame_ty = t.y;
+ view.frame_yaw = yawFromQuaternion(transform.transform.rotation);
+ } catch (const tf2::TransformException & ex) {
+ const auto now_ns = now().nanoseconds();
+ if (now_ns - last_tf_warn_ns_ > 2000000000LL) {
+ last_tf_warn_ns_ = now_ns;
+ RCLCPP_WARN(
+ get_logger(), "TF unavailable %s->%s for costmap query: %s",
+ map_frame_.c_str(), frame.c_str(), ex.what());
+ }
+ return view;
+ }
+ }
+
+ view.enabled = true;
+ return view;
+ }
+
+ bool costmapOccupiedAt(const CostmapView & view, double wx, double wy) const
+ {
+ if (!view.enabled || !view.msg) {
+ return false;
+ }
+
+ double cx = wx;
+ double cy = wy;
+ if (!view.same_frame) {
+ const double cos_yaw = std::cos(view.frame_yaw);
+ const double sin_yaw = std::sin(view.frame_yaw);
+ cx = view.frame_tx + cos_yaw * wx - sin_yaw * wy;
+ cy = view.frame_ty + sin_yaw * wx + cos_yaw * wy;
+ }
+
+ const double dx = cx - view.msg->info.origin.position.x;
+ const double dy = cy - view.msg->info.origin.position.y;
+ const double cos_origin = std::cos(view.origin_yaw);
+ const double sin_origin = std::sin(view.origin_yaw);
+ const double mx = cos_origin * dx + sin_origin * dy;
+ const double my = -sin_origin * dx + cos_origin * dy;
+ const int gx = static_cast(std::floor(mx / view.msg->info.resolution));
+ const int gy = static_cast(std::floor(my / view.msg->info.resolution));
+ if (gx < 0 || gy < 0 ||
+ gx >= static_cast(view.msg->info.width) ||
+ gy >= static_cast(view.msg->info.height))
+ {
+ return outside_costmap_is_obstacle_;
+ }
+
+ const auto idx = static_cast(gy * static_cast(view.msg->info.width) + gx);
+ if (idx >= view.msg->data.size()) {
+ return outside_costmap_is_obstacle_;
+ }
+
+ const int value = view.msg->data[idx];
+ if (value < 0) {
+ return unknown_is_obstacle_;
+ }
+ return value >= costmap_occupied_threshold_;
+ }
+
+ SearchResult search(
+ const Pose2D & start,
+ const Pose2D & goal,
+ const std::vector & occupied,
+ const std::chrono::steady_clock::time_point & search_start) const
+ {
+ SearchResult result;
+ std::priority_queue open_heap;
+ const int state_count = width_ * height_ * yaw_bins_ * 2;
+ std::vector best_cost(
+ static_cast(state_count), std::numeric_limits::infinity());
+ std::vector nodes;
+ nodes.reserve(4096);
+ auto last_progress_log = search_start;
+
+ Cell start_cell;
+ worldToGrid(start.x, start.y, start_cell, true);
+ Cell goal_cell;
+ worldToGrid(goal.x, goal.y, goal_cell, true);
+ const std::vector grid_heuristic =
+ use_grid_heuristic_ ? buildGridHeuristic(goal_cell, occupied) : std::vector{};
+ const int start_yaw_bin = yawToBin(start.yaw);
+ for (const int gear : {1, -1}) {
+ SearchNode node;
+ node.state = SearchState{start_cell.x, start_cell.y, start_yaw_bin, gear};
+ node.x = start.x;
+ node.y = start.y;
+ node.yaw = normalizeAngle(start.yaw);
+ node.gear = gear;
+ node.g = 0.0;
+ const int node_index = static_cast(nodes.size());
+ nodes.push_back(node);
+ const int state_idx = stateIndex(node.state);
+ best_cost[state_idx] = 0.0;
+ open_heap.push(QueueItem{
+ heuristic_weight_ * heuristic(node, goal, grid_heuristic), node_index});
+ }
+
+ while (!open_heap.empty() && result.iterations < max_iterations_) {
+ if (timedOut(search_start)) {
+ result.timed_out = true;
+ return result;
+ }
+
+ const int current_index = open_heap.top().node_index;
+ open_heap.pop();
+ const SearchNode current = nodes[static_cast(current_index)];
+ const int current_state_idx = stateIndex(current.state);
+ if (current.g > best_cost[current_state_idx] + 1e-9) {
+ continue;
+ }
+
+ ++result.iterations;
+ if (search_progress_log_period_ms_ > 0.0) {
+ const auto now_time = std::chrono::steady_clock::now();
+ const double progress_elapsed_ms =
+ std::chrono::duration(now_time - last_progress_log).count();
+ if (progress_elapsed_ms >= search_progress_log_period_ms_) {
+ last_progress_log = now_time;
+ const double total_elapsed_ms =
+ std::chrono::duration(now_time - search_start).count();
+ RCLCPP_INFO(
+ get_logger(),
+ "search_progress iterations=%d open=%zu explored=%zu current=(%.3f,%.3f,%.3f) "
+ "goal_distance=%.3f elapsed_ms=%.1f",
+ result.iterations, open_heap.size(), nodes.size(), current.x, current.y, current.yaw,
+ std::hypot(goal.x - current.x, goal.y - current.y), total_elapsed_ms);
+ }
+ }
+ if (goalReached(current, goal)) {
+ result.reached = true;
+ result.path = reconstructPath(nodes, current_index, start);
+ return result;
+ }
+
+ for (const int gear : {1, -1}) {
+ for (const int steering : {0, -1, 1}) {
+ SearchNode next;
+ if (!propagate(current, gear, steering, occupied, next)) {
+ continue;
+ }
+
+ next.parent = current_index;
+ next.g = current.g + transitionCost(current, gear, steering);
+ const int next_state_idx = stateIndex(next.state);
+ if (next.g + 1e-9 >= best_cost[next_state_idx]) {
+ continue;
+ }
+
+ best_cost[next_state_idx] = next.g;
+ const int next_index = static_cast(nodes.size());
+ nodes.push_back(std::move(next));
+ open_heap.push(QueueItem{
+ nodes[static_cast(next_index)].g +
+ heuristic_weight_ *
+ heuristic(nodes[static_cast(next_index)], goal, grid_heuristic),
+ next_index});
+ }
+ }
+ }
+
+ return result;
+ }
+
+ std::vector buildGridHeuristic(
+ const Cell & goal_cell,
+ const std::vector & occupied) const
+ {
+ std::vector cost(
+ static_cast(width_ * height_), std::numeric_limits::infinity());
+ if (!inBounds(goal_cell) || occupied[static_cast(index(goal_cell))]) {
+ return cost;
+ }
+
+ std::priority_queue open;
+ const int goal_index = index(goal_cell);
+ cost[static_cast(goal_index)] = 0.0;
+ open.push(GridQueueItem{0.0, goal_index});
+
+ constexpr int kNeighborCount = 8;
+ const int dx[kNeighborCount] = {1, -1, 0, 0, 1, 1, -1, -1};
+ const int dy[kNeighborCount] = {0, 0, 1, -1, 1, -1, 1, -1};
+
+ while (!open.empty()) {
+ const GridQueueItem current = open.top();
+ open.pop();
+ if (current.cost > cost[static_cast(current.cell_index)] + 1e-9) {
+ continue;
+ }
+
+ const Cell cell{current.cell_index % width_, current.cell_index / width_};
+ for (int i = 0; i < kNeighborCount; ++i) {
+ const Cell next{cell.x + dx[i], cell.y + dy[i]};
+ if (!inBounds(next) || occupied[static_cast(index(next))]) {
+ continue;
+ }
+ const double step_cost =
+ resolution_ * ((dx[i] == 0 || dy[i] == 0) ? 1.0 : std::sqrt(2.0));
+ const int next_index = index(next);
+ const double new_cost = current.cost + step_cost;
+ if (new_cost + 1e-9 >= cost[static_cast(next_index)]) {
+ continue;
+ }
+ cost[static_cast(next_index)] = new_cost;
+ open.push(GridQueueItem{new_cost, next_index});
+ }
+ }
+
+ return cost;
+ }
+
+ bool propagate(
+ const SearchNode & current,
+ int gear,
+ int steering,
+ const std::vector & occupied,
+ SearchNode & next) const
+ {
+ const int sample_count =
+ std::max(1, static_cast(std::ceil(primitive_length_ / collision_sample_step_)));
+ next.segment.clear();
+ next.segment.reserve(static_cast(sample_count));
+
+ for (int sample = 1; sample <= sample_count; ++sample) {
+ const double distance =
+ primitive_length_ * static_cast(sample) / static_cast(sample_count);
+ const Pose2D pose = integrate(current.x, current.y, current.yaw, gear, steering, distance);
+ if (!poseCollisionFree(pose, occupied)) {
+ return false;
+ }
+ next.segment.push_back(pose);
+ }
+
+ const Pose2D & final_pose = next.segment.back();
+ Cell cell;
+ if (!worldToGrid(final_pose.x, final_pose.y, cell)) {
+ return false;
+ }
+
+ next.x = final_pose.x;
+ next.y = final_pose.y;
+ next.yaw = normalizeAngle(final_pose.yaw);
+ next.gear = gear;
+ next.steering = steering;
+ next.state = SearchState{cell.x, cell.y, yawToBin(final_pose.yaw), gear};
+ return true;
+ }
+
+ Pose2D integrate(
+ double x,
+ double y,
+ double yaw,
+ int gear,
+ int steering,
+ double distance) const
+ {
+ Pose2D pose;
+ if (steering == 0) {
+ pose.x = x + static_cast(gear) * distance * std::cos(yaw);
+ pose.y = y + static_cast(gear) * distance * std::sin(yaw);
+ pose.yaw = normalizeAngle(yaw);
+ return pose;
+ }
+
+ const double curvature = static_cast(steering) / min_turning_radius_;
+ const double new_yaw = normalizeAngle(yaw + static_cast(gear) * curvature * distance);
+ pose.x = x + (std::sin(new_yaw) - std::sin(yaw)) / curvature;
+ pose.y = y + (std::cos(yaw) - std::cos(new_yaw)) / curvature;
+ pose.yaw = new_yaw;
+ return pose;
+ }
+
+ double transitionCost(
+ const SearchNode & current,
+ int gear,
+ int steering) const
+ {
+ double cost = primitive_length_;
+ if (gear < 0) {
+ cost *= reverse_penalty_;
+ }
+ if (steering != 0) {
+ cost += turn_penalty_;
+ }
+ if (gear != current.gear) {
+ cost += gear_change_penalty_;
+ }
+ cost += steering_change_penalty_ * std::abs(steering - current.steering);
+ return cost;
+ }
+
+ double heuristic(
+ const SearchNode & node,
+ const Pose2D & goal,
+ const std::vector & grid_heuristic) const
+ {
+ const double distance = std::hypot(goal.x - node.x, goal.y - node.y);
+ double cost_to_go = distance;
+ if (!grid_heuristic.empty()) {
+ const Cell cell{node.state.x, node.state.y};
+ if (inBounds(cell)) {
+ const double grid_cost = grid_heuristic[static_cast(index(cell))];
+ if (std::isfinite(grid_cost)) {
+ cost_to_go = std::max(cost_to_go, grid_cost);
+ }
+ }
+ }
+ if (!require_goal_yaw_) {
+ return cost_to_go;
+ }
+ const double yaw_error = std::abs(normalizeAngle(goal.yaw - node.yaw));
+ return cost_to_go + goal_yaw_heuristic_weight_ * min_turning_radius_ * yaw_error;
+ }
+
+ bool goalReached(const SearchNode & node, const Pose2D & goal) const
+ {
+ if (std::hypot(node.x - goal.x, node.y - goal.y) > goal_xy_tolerance_) {
+ return false;
+ }
+ return !require_goal_yaw_ ||
+ std::abs(normalizeAngle(node.yaw - goal.yaw)) <= goal_yaw_tolerance_;
+ }
+
+ std::vector reconstructPath(
+ const std::vector & nodes,
+ int goal_index,
+ const Pose2D & start) const
+ {
+ std::vector chain;
+ for (int index = goal_index; index >= 0; index = nodes[static_cast(index)].parent) {
+ chain.push_back(index);
+ if (nodes[static_cast(index)].parent < 0) {
+ break;
+ }
+ }
+ std::reverse(chain.begin(), chain.end());
+
+ std::vector path;
+ path.push_back(Pose2D{start.x, start.y, normalizeAngle(start.yaw)});
+ for (size_t i = 1; i < chain.size(); ++i) {
+ const auto & segment = nodes[static_cast(chain[i])].segment;
+ path.insert(path.end(), segment.begin(), segment.end());
+ }
+ return path;
+ }
+
+ bool timedOut(const std::chrono::steady_clock::time_point & start_time) const
+ {
+ const auto elapsed_ms =
+ std::chrono::duration(std::chrono::steady_clock::now() - start_time)
+ .count();
+ return elapsed_ms >= planning_timeout_ms_;
+ }
+
+ bool poseCollisionFree(const Pose2D & pose, const std::vector & occupied) const
+ {
+ Cell cell;
+ if (!worldToGrid(pose.x, pose.y, cell)) {
+ return false;
+ }
+ return !occupied[static_cast(index(cell))];
+ }
+
+ bool pathCollisionFree(const std::vector & path, const std::vector & occupied) const
+ {
+ return std::all_of(path.begin(), path.end(), [this, &occupied](const Pose2D & pose) {
+ return poseCollisionFree(pose, occupied);
+ });
+ }
+
+ void publishPath(const std::vector & points)
+ {
+ nav_msgs::msg::Path path;
+ path.header.frame_id = map_frame_;
+ path.header.stamp = toMsgTime(now());
+
+ for (const auto & point : points) {
+ path.poses.push_back(makePose(map_frame_, path.header.stamp, point.x, point.y, point.yaw));
+ }
+ plan_pub_->publish(path);
+ }
+
+ void publishEmptyPath()
+ {
+ nav_msgs::msg::Path path;
+ path.header.frame_id = map_frame_;
+ path.header.stamp = toMsgTime(now());
+ plan_pub_->publish(path);
+ }
+
+ bool currentPose(Pose2D & pose)
+ {
+ if (!latest_odom_) {
+ return false;
+ }
+
+ if (latest_odom_->header.stamp.sec != 0 || latest_odom_->header.stamp.nanosec != 0) {
+ const double age = (now() - rclcpp::Time(latest_odom_->header.stamp)).seconds();
+ if (age > max_odom_age_) {
+ return false;
+ }
+ }
+
+ pose.x = latest_odom_->pose.pose.position.x;
+ pose.y = latest_odom_->pose.pose.position.y;
+ pose.yaw = yawFromQuaternion(latest_odom_->pose.pose.orientation);
+ const std::string source_frame =
+ latest_odom_->header.frame_id.empty() ? map_frame_ : latest_odom_->header.frame_id;
+ if (source_frame != map_frame_) {
+ return transformPose2D(source_frame, pose);
+ }
+ return true;
+ }
+
+ bool transformPoseMsg(const geometry_msgs::msg::PoseStamped & msg, Pose2D & pose)
+ {
+ pose.x = msg.pose.position.x;
+ pose.y = msg.pose.position.y;
+ pose.yaw = yawFromQuaternion(msg.pose.orientation);
+ const std::string source_frame = msg.header.frame_id.empty() ? map_frame_ : msg.header.frame_id;
+ if (source_frame != map_frame_) {
+ return transformPose2D(source_frame, pose);
+ }
+ return true;
+ }
+
+ bool transformPose2D(const std::string & source_frame, Pose2D & pose)
+ {
+ try {
+ const auto transform = tf_buffer_.lookupTransform(
+ map_frame_, source_frame, tf2::TimePointZero,
+ tf2::durationFromSec(tf_timeout_));
+ const auto & t = transform.transform.translation;
+ const double transform_yaw = yawFromQuaternion(transform.transform.rotation);
+ const double cos_yaw = std::cos(transform_yaw);
+ const double sin_yaw = std::sin(transform_yaw);
+ const double x = pose.x;
+ const double y = pose.y;
+ pose.x = t.x + cos_yaw * x - sin_yaw * y;
+ pose.y = t.y + sin_yaw * x + cos_yaw * y;
+ pose.yaw = normalizeAngle(transform_yaw + pose.yaw);
+ return true;
+ } catch (const tf2::TransformException & ex) {
+ const auto now_ns = now().nanoseconds();
+ if (now_ns - last_tf_warn_ns_ > 2000000000LL) {
+ last_tf_warn_ns_ = now_ns;
+ RCLCPP_WARN(
+ get_logger(), "TF unavailable %s->%s: %s",
+ source_frame.c_str(), map_frame_.c_str(), ex.what());
+ }
+ return false;
+ }
+ }
+
+ bool worldToGrid(double x, double y, Cell & cell, bool clamp = false) const
+ {
+ int gx = static_cast(std::floor((x - map_min_x_) / resolution_));
+ int gy = static_cast(std::floor((y - map_min_y_) / resolution_));
+ if (clamp) {
+ gx = std::min(width_ - 1, std::max(0, gx));
+ gy = std::min(height_ - 1, std::max(0, gy));
+ cell = Cell{gx, gy};
+ return true;
+ }
+ cell = Cell{gx, gy};
+ return inBounds(cell);
+ }
+
+ std::pair gridToWorld(const Cell & cell) const
+ {
+ return {
+ map_min_x_ + (static_cast(cell.x) + 0.5) * resolution_,
+ map_min_y_ + (static_cast(cell.y) + 0.5) * resolution_};
+ }
+
+ bool inBounds(const Cell & cell) const
+ {
+ return cell.x >= 0 && cell.x < width_ && cell.y >= 0 && cell.y < height_;
+ }
+
+ int index(const Cell & cell) const
+ {
+ return cell.y * width_ + cell.x;
+ }
+
+ int yawToBin(double yaw) const
+ {
+ const double bin_size = 2.0 * kPi / static_cast(yaw_bins_);
+ return static_cast(std::floor((positiveAngle(yaw) + 0.5 * bin_size) / bin_size)) %
+ yaw_bins_;
+ }
+
+ int stateIndex(const SearchState & state) const
+ {
+ const int gear_index = state.gear > 0 ? 1 : 0;
+ return ((((state.y * width_) + state.x) * yaw_bins_ + state.yaw_bin) * 2) + gear_index;
+ }
+
+ void logRuntimeState()
+ {
+ std::ostringstream stream;
+ stream.precision(3);
+ stream << std::fixed;
+ stream << "planner_state ";
+
+ Pose2D pose;
+ const bool have_pose = currentPose(pose);
+ if (have_pose) {
+ stream << "pose=(" << pose.x << "," << pose.y << "," << pose.yaw << ")";
+ } else {
+ stream << "pose=unavailable";
+ if (!latest_odom_) {
+ stream << " odom=none";
+ } else if (latest_odom_->header.stamp.sec != 0 || latest_odom_->header.stamp.nanosec != 0) {
+ const double age = std::max(0.0, (now() - rclcpp::Time(latest_odom_->header.stamp)).seconds());
+ stream << " odom_age=" << age;
+ }
+ }
+
+ if (has_goal_) {
+ stream << " goal=(" << goal_pose_.x << "," << goal_pose_.y << "," << goal_pose_.yaw << ")";
+ if (have_pose) {
+ stream << " goal_distance=" << std::hypot(goal_pose_.x - pose.x, goal_pose_.y - pose.y);
+ stream << " yaw_error=" << std::abs(normalizeAngle(goal_pose_.yaw - pose.yaw));
+ }
+ } else {
+ stream << " goal=none";
+ }
+
+ stream << " path_points=" << last_path_.size();
+ stream << " obstacles=" << obstacles_.size();
+ stream << " pending_replan=" << (pending_replan_ ? "true" : "false");
+ stream << " map_bounds=(" << map_min_x_ << "," << map_min_y_ << ")-(" <<
+ map_max_x_ << "," << map_max_y_ << ")";
+ stream << " grid=" << width_ << "x" << height_ << "@" << resolution_;
+ if (latest_costmap_) {
+ stream << " costmap=" << latest_costmap_->info.width << "x" <<
+ latest_costmap_->info.height << "@" << latest_costmap_->info.resolution;
+ } else {
+ stream << " costmap=none";
+ }
+
+ RCLCPP_INFO(get_logger(), "%s", stream.str().c_str());
+ }
+
+ void publishStatus(const std::string & text)
+ {
+ std_msgs::msg::String msg;
+ msg.data = text;
+ status_pub_->publish(msg);
+ RCLCPP_INFO(get_logger(), "%s", text.c_str());
+ }
+
+ static std::string formatDouble(double value)
+ {
+ std::ostringstream stream;
+ stream.precision(3);
+ stream << std::fixed << value;
+ return stream.str();
+ }
+
+ std::string map_frame_;
+ std::string odom_topic_;
+ std::string goal_pose_topic_;
+ std::string obstacles_topic_;
+ std::string clear_obstacles_topic_;
+ std::string cancel_topic_;
+ std::string costmap_topic_;
+ std::string plan_topic_;
+ std::string status_topic_;
+ std::string obstacle_format_;
+ double map_min_x_{-5.0};
+ double map_min_y_{-5.0};
+ double map_max_x_{5.0};
+ double map_max_y_{5.0};
+ double resolution_{0.1};
+ bool fit_map_to_costmap_{true};
+ double default_obstacle_radius_{0.15};
+ double robot_radius_{0.14};
+ double localization_error_{0.10};
+ double latency_margin_{0.05};
+ double safety_margin_{0.05};
+ double unknown_obstacle_radius_{0.15};
+ bool accumulate_obstacles_{true};
+ double obstacle_merge_distance_{0.20};
+ int costmap_occupied_threshold_{50};
+ bool unknown_is_obstacle_{true};
+ bool outside_costmap_is_obstacle_{true};
+ bool inflate_static_map_{false};
+ bool plan_on_obstacle_update_{true};
+ int max_iterations_{50000};
+ double planning_timeout_ms_{100.0};
+ double replan_rate_{2.0};
+ double min_turning_radius_{0.40};
+ int yaw_bins_{72};
+ double primitive_length_{0.12};
+ double collision_sample_step_{0.03};
+ double goal_xy_tolerance_{0.20};
+ double goal_yaw_tolerance_{3.0 * kPi / 180.0};
+ bool require_goal_yaw_{true};
+ bool use_grid_heuristic_{true};
+ double heuristic_weight_{1.0};
+ double goal_yaw_heuristic_weight_{1.0};
+ double reverse_penalty_{1.3};
+ double turn_penalty_{0.05};
+ double gear_change_penalty_{0.20};
+ double steering_change_penalty_{0.02};
+ double max_odom_age_{0.80};
+ double tf_timeout_{0.05};
+ double state_log_period_{1.0};
+ double search_progress_log_period_ms_{500.0};
+ int width_{0};
+ int height_{0};
+ int64_t last_tf_warn_ns_{0};
+ bool has_goal_{false};
+ bool pending_replan_{false};
+ Pose2D goal_pose_;
+ std::vector obstacles_;
+ std::vector last_path_;
+ nav_msgs::msg::Odometry::SharedPtr latest_odom_;
+ nav_msgs::msg::OccupancyGrid::SharedPtr latest_costmap_;
+ tf2_ros::Buffer tf_buffer_;
+ tf2_ros::TransformListener tf_listener_;
+ rclcpp::Publisher::SharedPtr plan_pub_;
+ rclcpp::Publisher::SharedPtr status_pub_;
+ rclcpp::Subscription::SharedPtr odom_sub_;
+ rclcpp::Subscription::SharedPtr goal_sub_;
+ rclcpp::Subscription::SharedPtr obstacles_sub_;
+ rclcpp::Subscription::SharedPtr clear_obstacles_sub_;
+ rclcpp::Subscription::SharedPtr cancel_sub_;
+ rclcpp::Subscription::SharedPtr costmap_sub_;
+ rclcpp::TimerBase::SharedPtr replan_timer_;
+ rclcpp::TimerBase::SharedPtr state_log_timer_;
+};
+
+int main(int argc, char ** argv)
+{
+ rclcpp::init(argc, argv);
+ rclcpp::spin(std::make_shared());
+ rclcpp::shutdown();
return 0;
}
diff --git a/src/planner/src/planner_version.cpp b/src/planner/src/planner_version.cpp
index d9f04f4..f18be35 100644
--- a/src/planner/src/planner_version.cpp
+++ b/src/planner/src/planner_version.cpp
@@ -1,11 +1,11 @@
-#include
+ #include
-int main(int argc, char* argv[])
-{
- std::cout << "planner package: Nav2 minimal global planning stack" << std::endl;
- std::cout << " - slam_toolbox (online_async)" << std::endl;
- std::cout << " - planner_server (SmacPlannerHybrid)" << std::endl;
- std::cout << " - global_costmap + local_costmap" << std::endl;
- std::cout << " - lifecycle_manager" << std::endl;
- return 0;
-}
+ int main()
+ {
+ std::cout << "planner package: Nav2 minimal global planning stack" << std::endl;
+ std::cout << " - slam_toolbox (online_async)" << std::endl;
+ std::cout << " - planner_server (SmacPlannerHybrid)" << std::endl;
+ std::cout << " - global_costmap + local_costmap" << std::endl;
+ std::cout << " - lifecycle_manager" << std::endl;
+ return 0;
+ }
diff --git a/src/planner/操作手册.md b/src/planner/操作手册.md
new file mode 100644
index 0000000..de70542
--- /dev/null
+++ b/src/planner/操作手册.md
@@ -0,0 +1,238 @@
+# 自定义 Hybrid A* 导航操作手册
+
+本手册适用于仓库中的实车启动入口 `real_hybrid_astar.launch.py`。系统链路为：
+
+`STM32 底盘 -> EKF 里程计 -> AMCL/地图 -> 激光雷达 -> Hybrid A* -> Pure Pursuit -> 阿克曼命令转换`
+
+## 1. 安全和前置条件
+
+开始前确认：
+
+1. 机器人架起或周围有足够空旷区域，首次测试使用低速参数。
+2. 底盘串口、波特率和雷达设备路径正确。
+3. 真实地图 YAML 与对应 PGM/PNG 文件存在，地图坐标系为 `map`。
+4. 已测得机器人最小转弯半径，并在 `real_hybrid_astar.yaml` 中配置。
+5. 机器人初始位置在地图的可通行区域内。
+6. 只启动本手册中的导航链路。不要同时启动 Nav2 `controller_server`、
+ `origincar_bringup`、`path_follower_demo.py` 或 `udp_to_cmdvel.py`，否则会有多个
+ 节点同时发布 `/cmd_vel`。
+
+仓库中的 `maps/real_5x5.yaml` 是空房间合成地图，只用于软件链路测试，不能作为真实场地地图。
+
+## 2. 编译工作空间
+
+在机器人电脑上执行：
+
+```bash
+cd ~/liao_ws
+source /opt/ros/humble/setup.bash
+colcon build --symlink-install \
+ --packages-select \
+ origincar_msg origincar_description origincar_base \
+ lslidar_msgs lslidar_driver planner
+source install/setup.bash
+```
+
+如果只修改了 `planner`，也可以只编译该包及其依赖。每个新终端都要重新执行：
+
+```bash
+source /opt/ros/humble/setup.bash
+source ~/liao_ws/install/setup.bash
+```
+
+## 3. 检查地图
+
+地图 YAML 和图像必须成对存在。先检查尺寸、分辨率和原点：
+
+```bash
+cd ~/liao_ws
+python3 src/planner/scripts/map_info.py /实际路径/map.yaml
+```
+
+仓库测试地图的检查命令：
+
+```bash
+python3 src/planner/scripts/map_info.py \
+ src/planner/maps/real_5x5.yaml
+```
+
+5×5 米地图应显示 `size: 5.000 x 5.000 m` 和 `PASS`。真实地图尺寸不是 5×5 米时，启动前记录实际宽高，后面传给 `preflight_check.py`。
+
+## 4. 启动导航系统
+
+启动前替换地图、雷达参数和底盘串口：
+
+```bash
+ros2 launch planner real_hybrid_astar.launch.py \
+ map:=/实际地图/map.yaml \
+ lidar_params:=/实际雷达参数.yaml \
+ lidar_serial_port:=/dev/ttyCH343USB0 \
+ lidar_model:=N10 \
+ start_lidar:=true \
+ serial_port:=/dev/实际底盘串口 \
+ serial_baud:=115200 \
+ enable_motion:=false
+```
+
+使用仓库合成地图进行软件测试：
+
+```bash
+ros2 launch planner real_hybrid_astar.launch.py \
+ map:=$HOME/liao_ws/src/planner/maps/real_5x5.yaml \
+ lidar_params:=/实际雷达参数.yaml \
+ lidar_serial_port:=/dev/ttyCH343USB0 \
+ lidar_model:=N10 \
+ start_lidar:=true \
+ serial_port:=/dev/实际底盘串口 \
+ serial_baud:=115200 \
+ enable_motion:=false
+```
+
+保持该终端运行并观察日志。启动入口已经包含底盘、EKF、雷达、地图服务器、AMCL、规划器、Pure Pursuit 和 `/cmd_vel` 到 `/ackermann_cmd` 的转换节点。首次启动保持 `enable_motion:=false`，此时只验证规划，不执行路径。
+
+如果已经通过 `ros2 launch lslidar_driver ...` 单独启动雷达，必须把 `start_lidar:=true` 改为 `start_lidar:=false`，避免两个进程争抢同一串口。
+
+## 5. 检查 ROS 接口
+
+打开第二个终端并加载环境：
+
+```bash
+source /opt/ros/humble/setup.bash
+source ~/liao_ws/install/setup.bash
+```
+
+依次检查：
+
+```bash
+ros2 topic echo /scan --once
+ros2 topic echo /map --once
+ros2 topic echo /odom_combined --once
+ros2 run tf2_ros tf2_echo map base_footprint
+```
+
+确认以下条件：
+
+- `/scan` 持续有激光数据；
+- `/map` 的 `header.frame_id` 为 `map`；
+- `/odom_combined` 持续更新；
+- `map -> base_footprint` 和 `base_footprint -> laser` TF 可用。
+
+## 6. 初始化 AMCL 位姿
+
+在 RViz 中使用 **2D Pose Estimate** 设置机器人当前位置，或命令行发布（将坐标替换为实际值）：
+
+```bash
+ros2 topic pub --once /initialpose \
+ geometry_msgs/msg/PoseWithCovarianceStamped \
+ "{header: {frame_id: map}, pose: {pose: {position: {x: 0.54, y: 0.20}, orientation: {w: 1.0}}, covariance: [0.25, 0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.10]}}"
+```
+
+等待 AMCL 稳定，确认 `map -> base_footprint` TF 连续可查。
+
+## 7. 运行启动前检查
+
+默认按 5×5 米地图检查：
+
+```bash
+ros2 run planner preflight_check.py
+```
+
+检查通过后才允许发送目标。预检还会确认 `/cmd_vel` 只有一个发布者，并检查 `/ackermann_cmd` 转换节点与底盘订阅者。真实地图尺寸不同，例如 5.45×5.75 米：
+
+```bash
+ros2 run planner preflight_check.py --ros-args \
+ -p expected_map_width:=5.45 \
+ -p expected_map_height:=5.75
+```
+
+检查失败时，不要发送目标，先根据错误补齐地图、雷达、里程计或 TF。
+
+## 8. 发布目标点
+
+目标点使用 `map` 坐标系，单位为米：
+
+```bash
+ros2 run planner send_goal.py 1.00 1.00
+```
+
+第三个参数可指定终点航向角（弧度）：
+
+```bash
+ros2 run planner send_goal.py 1.00 1.00 1.57
+```
+
+默认只要求到达目标位置，位置误差小于约 0.15 米即停止。状态和路径不是持久话题，应先在其他终端启动监视，再发送目标：
+
+```bash
+ros2 topic echo /hybrid_astar_status
+ros2 topic echo /pure_pursuit_status
+ros2 topic echo /plan
+```
+
+只规划模式下，正常结果是规划状态 `plan_ok`、`/plan` 出现路径，且机器人不运动。`enable_motion:=true` 时，Pure Pursuit 才会发布实际速度，经过阿克曼转换后由底盘执行，机器人到达目标后停车。
+
+仓库地图的坐标范围是 `[-2.5, 2.5)`，目标必须严格位于范围内部并避开膨胀后的边界墙，不能使用 `(2.5, 2.5)`。
+
+首次只规划测试完成后，先取消目标并停止启动文件。确认转向方向、轴距和最大转角正确，再重新启动并显式允许低速运动：
+
+```bash
+ros2 launch planner real_hybrid_astar.launch.py \
+ map:=/实际地图/map.yaml \
+ lidar_params:=/实际雷达参数.yaml \
+ lidar_serial_port:=/dev/ttyCH343USB0 \
+ lidar_model:=N10 \
+ start_lidar:=true \
+ serial_port:=/dev/实际底盘串口 \
+ serial_baud:=115200 \
+ wheelbase:=0.143 \
+ max_steering_angle:=0.60 \
+ enable_motion:=true
+```
+
+重新启动后必须再次初始化 AMCL、运行 `preflight_check.py`，然后发布一个距离较近且无障碍的目标。第一次闭环测试应架起驱动轮或准备硬件急停，确认前进、倒车和左右转向符号正确后再落地运行。
+
+## 9. 取消导航和停车
+
+取消当前目标必须使用导航取消话题：
+
+```bash
+ros2 topic pub --once /navigation_cancel std_msgs/msg/Bool "{data: true}"
+```
+
+该命令会清除规划目标和跟踪路径，并持续保持停车。只发布一次零速度 `/cmd_vel` 不可靠，因为跟踪器可能在 20 Hz 下重新覆盖它。
+
+## 10. 常见问题
+
+### `preflight_check.py` 报无 `/map`
+
+检查 `map:=` 路径、YAML 引用的图像路径，以及 `map_server` 和生命周期管理器日志。
+
+### 地图尺寸检查失败
+
+用 `map_info.py` 查看实际尺寸，然后通过 `expected_map_width` 和 `expected_map_height` 传入真实尺寸。不要为了绕过检查而修改真实地图参数。
+
+### 无 `/scan` 或雷达 TF
+
+检查 `lidar_params` 是否匹配雷达型号、串口权限和 `base_footprint -> laser` 静态 TF。
+
+### 无 `map -> base_footprint`
+
+先在 RViz 设置 AMCL 初始位姿，确认 `/scan` 与地图大致重合；没有有效定位时禁止自动驾驶。
+
+### `/cmd_vel` 发布者数量不是 1
+
+执行：
+
+```bash
+ros2 topic info /cmd_vel -v
+```
+
+关闭重复启动的控制器，只保留 `topology_pure_pursuit` 发布速度指令。
+
+### 规划失败或机器人原地不动
+
+检查起点和目标是否在空闲区域，地图障碍物膨胀是否过大，机器人最小转弯半径是否填写正确，并查看 `/hybrid_astar_status` 的错误信息。
+
+## 11. 结束运行
+
+先发送 `/navigation_cancel`，确认机器人停止，再在启动终端按 `Ctrl+C` 关闭节点。实车测试结束后断开驱动电源或使底盘进入硬件急停状态。
diff --git a/test_results/goal_reach_debug_20260722_172834.json b/test_results/goal_reach_debug_20260722_172834.json
new file mode 100644
index 0000000..770028c
--- /dev/null
+++ b/test_results/goal_reach_debug_20260722_172834.json
@@ -0,0 +1,12664 @@
+{
+ "metadata": {
+ "started_wall_time": 1784712514.8502862,
+ "duration_sec": 30.068722632000004,
+ "output_path": "/home/sunrise/yiliao_ws/test_results/goal_reach_debug_20260722_172834.json"
+ },
+ "topics": {
+ "odom": "/odom",
+ "goal_pose": "/goal_pose",
+ "plan": "/plan",
+ "cmd_vel": "/planner_cmd_vel",
+ "ackermann_cmd": "/ackermann_cmd",
+ "planner_status": "/hybrid_astar_status",
+ "tracker_status": "/pure_pursuit_status"
+ },
+ "topic_endpoints": {
+ "/odom": {
+ "publisher_count": 1,
+ "subscriber_count": 4,
+ "publishers": [
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "planner_odom_map_tf",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ]
+ },
+ "/goal_pose": {
+ "publisher_count": 1,
+ "subscriber_count": 2,
+ "publishers": [
+ {
+ "node_name": "foxglove_bridge",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ }
+ ]
+ },
+ "/plan": {
+ "publisher_count": 2,
+ "subscriber_count": 2,
+ "publishers": [
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
+ },
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
+ }
+ ]
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+}
\ No newline at end of file
diff --git a/test_results/goal_reach_debug_20260722_173109.json b/test_results/goal_reach_debug_20260722_173109.json
new file mode 100644
index 0000000..9cc05df
--- /dev/null
+++ b/test_results/goal_reach_debug_20260722_173109.json
@@ -0,0 +1,12680 @@
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+ "未发现明显配置矛盾；请把 JSON 报告发给我继续分析。"
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+ }
+ ]
+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_163711.json b/test_results/reach_fail_debug_20260722_163711.json
new file mode 100644
index 0000000..952c6ff
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_163711.json
@@ -0,0 +1,5528 @@
+{
+ "metadata": {
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+ "duration_sec": 30.06486095800028,
+ "output_path": "/home/sunrise/yiliao_ws/test_results/reach_fail_debug_20260722_163711.json"
+ },
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+ "tracker_status": "/pure_pursuit_status"
+ },
+ "topic_endpoints": {
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+ "topic_type": "nav_msgs/msg/Odometry"
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+ {
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+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "ekf_filter_node",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
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+ }
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+ "subscribers": [
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+ "TF 查询失败 228 次；需要检查 map->odom 和 odom frame。"
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+ "yaw_error_to_goal": null,
+ "distance_to_plan_end": null,
+ "yaw_error_to_plan_end": null,
+ "plan_end_to_goal": null,
+ "plan_end_yaw_to_goal": null,
+ "cmd_vel": null,
+ "ackermann_cmd": null,
+ "planner_status": "",
+ "tracker_status": ""
+ },
+ {
+ "elapsed_sec": 30.06413466400045,
+ "pose_map": {
+ "available": false,
+ "reason": "tf_unavailable",
+ "source_frame": "odom"
+ },
+ "goal": null,
+ "plan_end": null,
+ "distance_to_goal": null,
+ "yaw_error_to_goal": null,
+ "distance_to_plan_end": null,
+ "yaw_error_to_plan_end": null,
+ "plan_end_to_goal": null,
+ "plan_end_yaw_to_goal": null,
+ "cmd_vel": null,
+ "ackermann_cmd": null,
+ "planner_status": "",
+ "tracker_status": ""
+ }
+ ]
+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_163934.json b/test_results/reach_fail_debug_20260722_163934.json
new file mode 100644
index 0000000..fac96cf
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_163934.json
@@ -0,0 +1,10717 @@
+{
+ "metadata": {
+ "started_wall_time": 1784709574.4937177,
+ "duration_sec": 30.145608000999346,
+ "output_path": "/home/sunrise/yiliao_ws/test_results/reach_fail_debug_20260722_163934.json"
+ },
+ "topics": {
+ "odom": "/odom",
+ "goal_pose": "/goal_pose",
+ "plan": "/plan",
+ "cmd_vel": "/cmd_vel",
+ "ackermann_cmd": "/ackermann_cmd",
+ "planner_status": "/hybrid_astar_status",
+ "tracker_status": "/pure_pursuit_status"
+ },
+ "topic_endpoints": {
+ "/odom": {
+ "publisher_count": 3,
+ "subscriber_count": 8,
+ "publishers": [
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "planner_odom_map_tf",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
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+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "ekf_filter_node",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "ekf_filter_node",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
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+ {
+ "node_name": "ekf_filter_node",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "ekf_filter_node",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
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+ },
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+ "subscribers": [
+ {
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+ },
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+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/Twist"
+ },
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/Twist"
+ }
+ ]
+ },
+ "/ackermann_cmd": {
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+ "publishers": [],
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+ {
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+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
+ }
+ ]
+ },
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+ "subscriber_count": 1,
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+ ],
+ "subscribers": [
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+ }
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+ "node_namespace": "/",
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+ }
+ ],
+ "subscribers": [
+ {
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+ }
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+ "iterations": 131,
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+ ]
+ },
+ "metrics": {
+ "sample_count": 298,
+ "goal_count": 5,
+ "plan_count": 5,
+ "planner_status_count": 15,
+ "tracker_status_count": 5,
+ "min_distance_to_goal": 1.1043541046029437,
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+ "final_plan_end_to_goal": 0.09775775438188386,
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+ "tf_failures": 0,
+ "saw_plan_ok": true,
+ "saw_plan_failed": false,
+ "saw_goal_reached": false,
+ "saw_align_goal_yaw": false,
+ "saw_no_recent_odom": false
+ },
+ "diagnostic_hints": [
+ "planner 的 goal_xy_tolerance 大于 tracker 的 goal_tolerance；planner 可能发布一个离原始目标较远的路径终点。",
+ "planner 的 goal_yaw_tolerance 大于 tracker 的 goal_yaw_tolerance；planner 认为合格的终点 yaw，tracker 可能继续调整。",
+ "tracker 的 min_linear_speed 数值大于 goal_tolerance；接近终点时可能仍以较高速度越过目标。",
+ "最新 /plan 的终点到 /goal_pose 的距离大于 tracker 到点半径；tracker 跟随 /plan 时不会到达你原始输入的目标点。"
+ ],
+ "events": {
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+ "frame_id": "map",
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+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_165021.json b/test_results/reach_fail_debug_20260722_165021.json
new file mode 100644
index 0000000..6bdf405
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_165021.json
@@ -0,0 +1,11838 @@
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+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_170425.json b/test_results/reach_fail_debug_20260722_170425.json
new file mode 100644
index 0000000..9255ddd
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_170425.json
@@ -0,0 +1,8478 @@
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+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_170521.json b/test_results/reach_fail_debug_20260722_170521.json
new file mode 100644
index 0000000..a83f317
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_170521.json
@@ -0,0 +1,10166 @@
+{
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diff --git a/test_results/reach_fail_debug_20260722_170728.json b/test_results/reach_fail_debug_20260722_170728.json
new file mode 100644
index 0000000..e8e32a5
--- /dev/null
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+ "plan_end_to_goal": 0.04867885459522371,
+ "plan_end_yaw_to_goal": 5.551115123125783e-17,
+ "cmd_vel": {
+ "linear_x": 0.0875,
+ "angular_z": 0.35
+ },
+ "ackermann_cmd": {
+ "speed": 0.08749999850988388,
+ "steering_angle": 0.5195767879486084
+ },
+ "planner_status": "plan_ok hybrid_astar points=73 iterations=27 obstacles=0 planning_ms=158.885 goal_error=0.049 yaw_error=0.000",
+ "tracker_status": "external_plan_ok points=73 length=2.160 goal=(1.452,1.507,0.000)"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_170806.json b/test_results/reach_fail_debug_20260722_170806.json
new file mode 100644
index 0000000..5b70e22
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_170806.json
@@ -0,0 +1,11253 @@
+{
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+ "duration_sec": 30.140174545000264,
+ "output_path": "/home/sunrise/yiliao_ws/test_results/reach_fail_debug_20260722_170806.json"
+ },
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+ "cmd_vel": "/planner_cmd_vel",
+ "ackermann_cmd": "/ackermann_cmd",
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+ "tracker_status": "/pure_pursuit_status"
+ },
+ "topic_endpoints": {
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+ },
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+ "steering_angle": 0.09057723730802536
+ },
+ "planner_status": "plan_ok hybrid_astar points=69 iterations=242 obstacles=0 planning_ms=185.361 goal_error=0.045 yaw_error=0.984",
+ "tracker_status": "external_plan_ok points=69 length=2.040 goal=(0.005,0.045,0.984)"
+ },
+ {
+ "elapsed_sec": 30.13955517100112,
+ "pose_map": {
+ "available": true,
+ "frame_id": "map",
+ "source_frame": "odom",
+ "x": 0.6533724665641785,
+ "y": 1.0033272951841354,
+ "yaw": 0.8597542643547058,
+ "odom_age_sec": 0.031650518,
+ "odom_linear_x": -0.09099999815225601,
+ "odom_angular_z": 0.10160456597805023
+ },
+ "goal": {
+ "elapsed_sec": 11.510467646001416,
+ "frame_id": "map",
+ "x": 0.0,
+ "y": 0.0,
+ "yaw": 0.0
+ },
+ "plan_end": {
+ "x": 0.005408366623766536,
+ "y": 0.044796603599786225,
+ "yaw": 0.9836291480064391
+ },
+ "distance_to_goal": 1.1973141782028942,
+ "yaw_error_to_goal": 0.8597542643547058,
+ "distance_to_plan_end": 1.156995488980298,
+ "yaw_error_to_plan_end": 0.12387488365173327,
+ "plan_end_to_goal": 0.045121902925446884,
+ "plan_end_yaw_to_goal": 0.9836291480064391,
+ "cmd_vel": {
+ "linear_x": -0.10170777646578139,
+ "angular_z": -0.062015379409347086
+ },
+ "ackermann_cmd": {
+ "speed": -0.10170777887105942,
+ "steering_angle": 0.08697296679019928
+ },
+ "planner_status": "plan_ok hybrid_astar points=69 iterations=242 obstacles=0 planning_ms=185.361 goal_error=0.045 yaw_error=0.984",
+ "tracker_status": "external_plan_ok points=69 length=2.040 goal=(0.005,0.045,0.984)"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_171435.json b/test_results/reach_fail_debug_20260722_171435.json
new file mode 100644
index 0000000..283b0a6
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_171435.json
@@ -0,0 +1,11103 @@
+{
+ "metadata": {
+ "started_wall_time": 1784711675.3986046,
+ "duration_sec": 30.008186054999896,
+ "output_path": "/home/sunrise/yiliao_ws/test_results/reach_fail_debug_20260722_171435.json"
+ },
+ "topics": {
+ "odom": "/odom",
+ "goal_pose": "/goal_pose",
+ "plan": "/plan",
+ "cmd_vel": "/planner_cmd_vel",
+ "ackermann_cmd": "/ackermann_cmd",
+ "planner_status": "/hybrid_astar_status",
+ "tracker_status": "/pure_pursuit_status"
+ },
+ "topic_endpoints": {
+ "/odom": {
+ "publisher_count": 1,
+ "subscriber_count": 4,
+ "publishers": [
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "planner_odom_map_tf",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ]
+ },
+ "/goal_pose": {
+ "publisher_count": 1,
+ "subscriber_count": 3,
+ "publishers": [
+ {
+ "node_name": "foxglove_bridge",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ },
+ {
+ "node_name": "foxglove_bridge",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ },
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ }
+ ]
+ },
+ "/plan": {
+ "publisher_count": 2,
+ "subscriber_count": 3,
+ "publishers": [
+ {
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+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
+ },
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
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+ ],
+ "subscribers": [
+ {
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+ "node_namespace": "/",
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+ },
+ {
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+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
+ },
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
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+ "publishers": [
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
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+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/Twist"
+ },
+ {
+ "node_name": "planner_ackermann_cmd_bridge",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/Twist"
+ }
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+ },
+ "/ackermann_cmd": {
+ "publisher_count": 1,
+ "subscriber_count": 2,
+ "publishers": [
+ {
+ "node_name": "planner_ackermann_cmd_bridge",
+ "node_namespace": "/",
+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
+ },
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
+ }
+ ]
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+ "subscriber_count": 1,
+ "publishers": [
+ {
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+ "node_namespace": "/",
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+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "goal_reach_debug_collector",
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+ "node_namespace": "/",
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+ }
+ ],
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+ {
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+ "node_namespace": "/",
+ "topic_type": "std_msgs/msg/String"
+ }
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+ }
+ },
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+ "service": "/grid_astar_theta_planner/get_parameters",
+ "values": {
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+ "goal_yaw_heuristic_weight": 0.0,
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+ }
+ },
+ "tracker": {
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+ "slowdown_distance": 0.5,
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+ }
+ },
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+ "yaw": 0.0
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+ "tracker_status": "stop no_recent_odom",
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+ "saw_plan_failed": false,
+ "saw_goal_reached": false,
+ "saw_align_goal_yaw": false,
+ "saw_no_recent_odom": true
+ },
+ "diagnostic_hints": [
+ "没有采集到 /plan；planner 可能未规划成功或采集脚本启动太晚。",
+ "TF 查询失败 21 次；需要检查 map->odom 和 odom frame。",
+ "采集期间没有观察到速度命令；如果正在做实车到点测试，请确认 launch 使用 enable_motion:=true，且采集的是 tracker 实际输出话题。"
+ ],
+ "events": {
+ "goals": [
+ {
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+ "y": 0.0,
+ "yaw": 0.0,
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+ "odom_angular_z": 0.0
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+ "goal": {
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+ "x": 1.5,
+ "y": 1.5,
+ "yaw": 0.0
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+ "plan_end_yaw_to_goal": null,
+ "cmd_vel": {
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+ "angular_z": 0.0
+ },
+ "ackermann_cmd": {
+ "speed": 0.0,
+ "steering_angle": 0.0
+ },
+ "planner_status": "",
+ "tracker_status": "stop no_recent_odom"
+ },
+ {
+ "elapsed_sec": 29.80719439800123,
+ "pose_map": {
+ "available": true,
+ "frame_id": "map",
+ "source_frame": "odom",
+ "x": 0.0,
+ "y": 0.0,
+ "yaw": 0.0,
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+ "odom_angular_z": 0.0
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+ "goal": {
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+ "frame_id": "map",
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+ "y": 1.5,
+ "yaw": 0.0
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+ "distance_to_goal": 2.1213203435596424,
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+ "yaw_error_to_plan_end": null,
+ "plan_end_to_goal": null,
+ "plan_end_yaw_to_goal": null,
+ "cmd_vel": {
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+ "angular_z": 0.0
+ },
+ "ackermann_cmd": {
+ "speed": 0.0,
+ "steering_angle": 0.0
+ },
+ "planner_status": "",
+ "tracker_status": "stop no_recent_odom"
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+ {
+ "elapsed_sec": 29.90719189400079,
+ "pose_map": {
+ "available": true,
+ "frame_id": "map",
+ "source_frame": "odom",
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+ "y": 0.0,
+ "yaw": 0.0,
+ "odom_age_sec": 0.030190144000000002,
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+ "odom_angular_z": 0.0
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+ "goal": {
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+ "frame_id": "map",
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+ "y": 1.5,
+ "yaw": 0.0
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+ "plan_end": null,
+ "distance_to_goal": 2.1213203435596424,
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+ "yaw_error_to_plan_end": null,
+ "plan_end_to_goal": null,
+ "plan_end_yaw_to_goal": null,
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+ "angular_z": 0.0
+ },
+ "ackermann_cmd": {
+ "speed": 0.0,
+ "steering_angle": 0.0
+ },
+ "planner_status": "",
+ "tracker_status": "stop no_recent_odom"
+ },
+ {
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+ "pose_map": {
+ "available": true,
+ "frame_id": "map",
+ "source_frame": "odom",
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+ "y": 0.0,
+ "yaw": 0.0,
+ "odom_age_sec": 0.039162583,
+ "odom_linear_x": 0.0,
+ "odom_angular_z": 0.0
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+ "goal": {
+ "elapsed_sec": 14.473757830000977,
+ "frame_id": "map",
+ "x": 1.5,
+ "y": 1.5,
+ "yaw": 0.0
+ },
+ "plan_end": null,
+ "distance_to_goal": 2.1213203435596424,
+ "yaw_error_to_goal": 0.0,
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+ "yaw_error_to_plan_end": null,
+ "plan_end_to_goal": null,
+ "plan_end_yaw_to_goal": null,
+ "cmd_vel": {
+ "linear_x": 0.0,
+ "angular_z": 0.0
+ },
+ "ackermann_cmd": {
+ "speed": 0.0,
+ "steering_angle": 0.0
+ },
+ "planner_status": "",
+ "tracker_status": "stop no_recent_odom"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/test_results/reach_fail_debug_20260722_172319.json b/test_results/reach_fail_debug_20260722_172319.json
new file mode 100644
index 0000000..34c4f47
--- /dev/null
+++ b/test_results/reach_fail_debug_20260722_172319.json
@@ -0,0 +1,9127 @@
+{
+ "metadata": {
+ "started_wall_time": 1784712199.8578296,
+ "duration_sec": 30.15390686200044,
+ "output_path": "/home/sunrise/yiliao_ws/test_results/reach_fail_debug_20260722_172319.json"
+ },
+ "topics": {
+ "odom": "/odom",
+ "goal_pose": "/goal_pose",
+ "plan": "/plan",
+ "cmd_vel": "/planner_cmd_vel",
+ "ackermann_cmd": "/ackermann_cmd",
+ "planner_status": "/hybrid_astar_status",
+ "tracker_status": "/pure_pursuit_status"
+ },
+ "topic_endpoints": {
+ "/odom": {
+ "publisher_count": 1,
+ "subscriber_count": 4,
+ "publishers": [
+ {
+ "node_name": "origincar_base",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "planner_odom_map_tf",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "topology_pure_pursuit",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Odometry"
+ }
+ ]
+ },
+ "/goal_pose": {
+ "publisher_count": 2,
+ "subscriber_count": 3,
+ "publishers": [
+ {
+ "node_name": "_NODE_NAME_UNKNOWN_",
+ "node_namespace": "_NODE_NAMESPACE_UNKNOWN_",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ },
+ {
+ "node_name": "foxglove_bridge",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "foxglove_bridge",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ },
+ {
+ "node_name": "grid_astar_theta_planner",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/PoseStamped"
+ }
+ ]
+ },
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+ "subscriber_count": 3,
+ "publishers": [
+ {
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+ "node_namespace": "/",
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+ {
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+ "node_namespace": "/",
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+ }
+ ],
+ "subscribers": [
+ {
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+ "node_namespace": "/",
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+ "node_namespace": "/",
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+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "nav_msgs/msg/Path"
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+ {
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+ "node_namespace": "/",
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+ ],
+ "subscribers": [
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+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "geometry_msgs/msg/Twist"
+ }
+ ]
+ },
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+ "publisher_count": 1,
+ "subscriber_count": 2,
+ "publishers": [
+ {
+ "node_name": "planner_ackermann_cmd_bridge",
+ "node_namespace": "/",
+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "origincar_base",
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+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
+ },
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "ackermann_msgs/msg/AckermannDriveStamped"
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+ "node_namespace": "/",
+ "topic_type": "std_msgs/msg/String"
+ }
+ ],
+ "subscribers": [
+ {
+ "node_name": "goal_reach_debug_collector",
+ "node_namespace": "/",
+ "topic_type": "std_msgs/msg/String"
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+ }
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+ }
+ },
+ "tracker": {
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+ "service": "/topology_pure_pursuit/get_parameters",
+ "error": "parameter_service_unavailable"
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+ "latest": {
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+ "length": 0.0,
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+ "tracker_status": "stop no_recent_odom",
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+ "metrics": {
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+ "没有采集到 /plan；planner 可能未规划成功或采集脚本启动太晚。",
+ "TF 查询失败 21 次；需要检查 map->odom 和 odom frame。",
+ "采集期间没有观察到速度命令；如果正在做实车到点测试，请确认 launch 使用 enable_motion:=true，且采集的是 tracker 实际输出话题。"
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+ "events": {
+ "goals": [],
+ "plans": [],
+ "planner_status": [],
+ "tracker_status": [
+ {
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+ "data": "ready graph_nodes=0 graph_edges=0 motion_enabled=True"
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+}
\ No newline at end of file