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obstacle_avoidance_planner package from autodrrt repo

autonomous_emergency_braking control_performance_analysis control_validator external_cmd_selector joy_controller lane_departure_checker mpc_lateral_controller obstacle_collision_checker operation_mode_transition_manager pid_longitudinal_controller predicted_path_checker pure_pursuit shift_decider trajectory_follower_base trajectory_follower_node vehicle_cmd_gate diagnostic_converter kinematic_evaluator localization_evaluator planning_evaluator ekf_localizer geo_pose_projector gyro_odometer ar_tag_based_localizer landmark_manager localization_error_monitor localization_util ndt_scan_matcher pose2twist pose_initializer pose_instability_detector stop_filter tree_structured_parzen_estimator twist2accel yabloc_common yabloc_image_processing yabloc_monitor yabloc_particle_filter yabloc_pose_initializer map_height_fitter map_loader map_projection_loader map_tf_generator lanelet2_map_preprocessor ros2_bevdet ros2_bevformer bevfusion bytetrack cluster_merger compare_map_segmentation crosswalk_traffic_light_estimator detected_object_feature_remover detected_object_validation detection_by_tracker elevation_map_loader euclidean_cluster front_vehicle_velocity_estimator ground_segmentation heatmap_visualizer image_projection_based_fusion lidar_apollo_instance_segmentation lidar_apollo_segmentation_tvm lidar_apollo_segmentation_tvm_nodes lidar_centerpoint lidar_centerpoint_tvm map_based_prediction multi_object_tracker object_merger object_range_splitter object_velocity_splitter occupancy_grid_map_outlier_filter probabilistic_occupancy_grid_map radar_crossing_objects_noise_filter radar_fusion_to_detected_object radar_object_clustering radar_object_tracker radar_tracks_msgs_converter shape_estimation simple_object_merger tensorrt_classifier tensorrt_yolo tensorrt_yolox tracking_object_merger traffic_light_arbiter traffic_light_classifier traffic_light_fine_detector traffic_light_map_based_detector traffic_light_multi_camera_fusion traffic_light_occlusion_predictor traffic_light_ssd_fine_detector traffic_light_visualization behavior_path_avoidance_by_lane_change_module behavior_path_avoidance_module behavior_path_external_request_lane_change_module behavior_path_goal_planner_module behavior_path_lane_change_module behavior_path_planner behavior_path_planner_common behavior_path_side_shift_module behavior_path_start_planner_module behavior_velocity_blind_spot_module behavior_velocity_crosswalk_module behavior_velocity_detection_area_module behavior_velocity_intersection_module behavior_velocity_no_drivable_lane_module behavior_velocity_no_stopping_area_module behavior_velocity_occlusion_spot_module behavior_velocity_out_of_lane_module behavior_velocity_planner behavior_velocity_planner_common behavior_velocity_run_out_module behavior_velocity_speed_bump_module behavior_velocity_stop_line_module behavior_velocity_template_module behavior_velocity_traffic_light_module behavior_velocity_virtual_traffic_light_module behavior_velocity_walkway_module costmap_generator external_velocity_limit_selector freespace_planner freespace_planning_algorithms mission_planner motion_velocity_smoother objects_of_interest_marker_interface obstacle_avoidance_planner obstacle_cruise_planner obstacle_stop_planner obstacle_velocity_limiter path_smoother planning_debug_tools planning_test_utils planning_topic_converter planning_validator route_handler rtc_interface rtc_replayer bezier_sampler frenet_planner path_sampler sampler_common scenario_selector static_centerline_optimizer surround_obstacle_checker gnss_poser image_diagnostics image_transport_decompressor imu_corrector livox_tag_filter pointcloud_preprocessor radar_scan_to_pointcloud2 radar_static_pointcloud_filter radar_threshold_filter radar_tracks_noise_filter tier4_pcl_extensions vehicle_velocity_converter autoware_auto_msgs_adapter bluetooth_monitor component_state_monitor default_ad_api ad_api_adaptors ad_api_visualizers automatic_pose_initializer diagnostic_graph_aggregator dummy_diag_publisher dummy_infrastructure duplicated_node_checker emergency_handler mrm_comfortable_stop_operator mrm_emergency_stop_operator system_error_monitor system_monitor topic_state_monitor velodyne_monitor accel_brake_map_calibrator external_cmd_converter raw_vehicle_cmd_converter steer_offset_estimator vehicle_info_util launch launch_ros autoware_ad_api_specs autoware_adapi_v1_msgs autoware_adapi_version_msgs autoware_auto_common autoware_auto_geometry autoware_auto_control_msgs autoware_auto_geometry_msgs autoware_auto_mapping_msgs autoware_auto_msgs autoware_auto_perception_msgs autoware_auto_planning_msgs autoware_auto_system_msgs autoware_auto_vehicle_msgs autoware_auto_perception_rviz_plugin autoware_auto_tf2 autoware_cmake autoware_lint_common autoware_utils lanelet2_extension autoware_common_msgs autoware_control_msgs autoware_localization_msgs autoware_map_msgs autoware_perception_msgs autoware_planning_msgs autoware_sensing_msgs autoware_system_msgs autoware_vehicle_msgs autoware_point_types autoware_testing bag_time_manager_rviz_plugin component_interface_specs component_interface_tools component_interface_utils cuda_utils fake_test_node geography_utils global_parameter_loader glog_component goal_distance_calculator grid_map_utils interpolation kalman_filter motion_utils object_recognition_utils osqp_interface path_distance_calculator perception_utils polar_grid qp_interface rtc_manager_rviz_plugin signal_processing tensorrt_common tier4_adapi_rviz_plugin tier4_api_utils tier4_automatic_goal_rviz_plugin tier4_autoware_utils tier4_calibration_rviz_plugin tier4_camera_view_rviz_plugin tier4_control_rviz_plugin tier4_datetime_rviz_plugin tier4_debug_rviz_plugin tier4_debug_tools tier4_localization_rviz_plugin tier4_perception_rviz_plugin tier4_planning_rviz_plugin tier4_screen_capture_rviz_plugin tier4_simulated_clock_rviz_plugin tier4_state_rviz_plugin tier4_system_rviz_plugin tier4_target_object_type_rviz_plugin tier4_traffic_light_rviz_plugin tier4_vehicle_rviz_plugin time_utils simulator_compatibility_test traffic_light_recognition_marker_publisher traffic_light_utils tvm_utility dma_customer_msg dma_transfer eagleye_coordinate eagleye_navigation eagleye_msgs eagleye_rt eagleye_can_velocity_converter eagleye_fix2kml eagleye_geo_pose_converter eagleye_geo_pose_fusion eagleye_gnss_converter eagleye_tf llh_converter morai_msgs mussp ndt_omp orocos_kdl python_orocos_kdl pointcloud_to_laserscan rtklib_bridge rtklib_msgs autoware_external_api_msgs autoware_iv_external_api_adaptor autoware_iv_internal_api_adaptor awapi_awiv_adapter tier4_api_msgs tier4_auto_msgs_converter tier4_control_msgs tier4_debug_msgs tier4_external_api_msgs tier4_hmi_msgs tier4_localization_msgs tier4_map_msgs tier4_perception_msgs tier4_planning_msgs tier4_rtc_msgs tier4_simulation_msgs tier4_system_msgs tier4_v2x_msgs tier4_vehicle_msgs io_opt tier4_autoware_api_launch tier4_control_launch tier4_localization_launch tier4_map_launch tier4_perception_launch tier4_planning_launch tier4_sensing_launch tier4_simulator_launch tier4_system_launch tier4_vehicle_launch fastrtps cyclonedds lanelet2 lanelet2_core lanelet2_examples lanelet2_io lanelet2_maps lanelet2_matching lanelet2_projection lanelet2_python lanelet2_routing lanelet2_traffic_rules lanelet2_validation sophus angles behaviortree_cpp_v3 bond bond_core bondcpp bondpy smclib test_bond cudnn_cmake_module diagnostic_aggregator diagnostic_common_diagnostics diagnostic_updater diagnostics self_test filters geodesy geographic_info geographic_msgs grid_map grid_map_cmake_helpers grid_map_core grid_map_costmap_2d grid_map_cv grid_map_demos grid_map_filters grid_map_loader grid_map_msgs grid_map_octomap grid_map_pcl grid_map_ros grid_map_rviz_plugin grid_map_sdf grid_map_visualization mrt_cmake_modules nav2_amcl nav2_behavior_tree nav2_behaviors nav2_bringup nav2_bt_navigator nav2_collision_monitor nav2_common nav2_controller nav2_core nav2_costmap_2d costmap_queue dwb_core dwb_critics dwb_msgs dwb_plugins nav2_dwb_controller nav_2d_msgs nav_2d_utils nav2_lifecycle_manager nav2_map_server nav2_msgs nav2_navfn_planner nav2_planner nav2_regulated_pure_pursuit_controller nav2_rotation_shim_controller nav2_rviz_plugins nav2_simple_commander nav2_smac_planner nav2_smoother nav2_system_tests nav2_theta_star_planner nav2_util nav2_velocity_smoother nav2_voxel_grid nav2_waypoint_follower navigation2 dynamic_edt_3d octomap octovis octomap_msgs osqp_vendor pacmod3_msgs pcl_msgs pcl_conversions pcl_ros perception_pcl point_cloud_msg_wrapper radar_msgs can_msgs rqt_tf_tree tensorrt_cmake_module topic_tools topic_tools_interfaces tvm_vendor cv_bridge image_geometry opencv_tests vision_opencv xacro rviz2 rviz_assimp_vendor rviz_common rviz_default_plugins rviz_ogre_vendor rviz_rendering rviz_rendering_tests rviz_visual_testing_framework dummy_perception_publisher fault_injection simple_planning_simulator classformsg node_v2x image_view v4l2_camera can_interface_custom cgi430_can_driver cgi610_driver ARS408_driver data_format_dump data_preprocess_launch lidar_centerpoint_collect lidar_saver message_sync time_cal camera_calibration direct_visual_lidar_calibration multi_lidar_calibration

Package Summary

Tags No category tags.
Version 0.1.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/ieiauto/autodrrt.git
VCS Type git
VCS Version main
Last Updated 2024-09-19
Dev Status UNMAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

The obstacle_avoidance_planner package

Additional Links

No additional links.

Maintainers

  • Takayuki Murooka
  • Kosuke Takeuchi
  • Takamasa Horibe

Authors

  • Takayuki Murooka

Obstacle Avoidance Planner

Purpose

This package generates a trajectory that is kinematically-feasible to drive and collision-free based on the input path, drivable area. Only position and orientation of trajectory are updated in this module, and velocity is just taken over from the one in the input path.

Feature

This package is able to

  • make the trajectory inside the drivable area as much as possible
    • NOTE: Static obstacles to avoid can be removed from the drivable area.
  • insert stop point before the planned footprint will be outside the drivable area

Note that the velocity is just taken over from the input path.

Inputs / Outputs

input

Name Type Description
~/input/path autoware_auto_planning_msgs/msg/Path Reference path and the corresponding drivable area
~/input/odometry nav_msgs/msg/Odometry Current Velocity of ego vehicle

output

Name Type Description
~/output/trajectory autoware_auto_planning_msgs/msg/Trajectory Optimized trajectory that is feasible to drive and collision-free

Flowchart

Flowchart of functions is explained here.

@startuml
title pathCallback
start

:isDataReady;

:createPlannerData;

group generateOptimizedTrajectory
  group optimizeTrajectory
    :check replan;
    if (replanning required?) then (yes)
      :getEBTrajectory;
      :getModelPredictiveTrajectory;
      if (optimization failed?) then (no)
      else (yes)
        :send previous\n trajectory;
      endif
    else (no)
      :send previous\n trajectory;
    endif
  end group

  :applyInputVelocity;
  :insertZeroVelocityOutsideDrivableArea;
  :publishDebugMarkerOfOptimization;
end group


:extendTrajectory;

:setZeroVelocityAfterStopPoint;

:publishDebugData;

stop
@enduml

createPlannerData

The following data for planning is created.

struct PlannerData
{
  // input
  Header header;
  std::vector<TrajectoryPoint> traj_points; // converted from the input path
  std::vector<geometry_msgs::msg::Point> left_bound;
  std::vector<geometry_msgs::msg::Point> right_bound;

  // ego
  geometry_msgs::msg::Pose ego_pose;
  double ego_vel;
};

check replan

When one of the following conditions are met, trajectory optimization will be executed. Otherwise, previously optimized trajectory is used with updating the velocity from the latest input path.

max_path_shape_around_ego_lat_dist

  • Ego moves longer than replan.max_ego_moving_dist in one cycle. (default: 3.0 [m])
    • This is for when the ego pose is set again in the simulation.
  • Trajectory’s end, which is considered as the goal pose, moves longer than replan.max_goal_moving_dist in one cycle. (default: 15.0 [ms])
    • When the goal pose is set again, the planning should be reset.
  • Time passes. (default: 1.0 [s])
    • The optimization is skipped for a while sine the optimization is sometimes heavy.
  • The input path changes laterally longer than replan.max_path_shape_around_ego_lat_dist in one cycle. (default: 2.0)

getModelPredictiveTrajectory

This module makes the trajectory kinematically-feasible and collision-free. We define vehicle pose in the frenet coordinate, and minimize tracking errors by optimization. This optimization considers vehicle kinematics and collision checking with road boundary and obstacles. To decrease the computation cost, the optimization is applied to the shorter trajectory (default: 50 [m]) than the whole trajectory, and concatenate the remained trajectory with the optimized one at last.

The trajectory just in front of the ego must not be changed a lot so that the steering wheel will be stable. Therefore, we use the previously generated trajectory in front of the ego.

Optimization center on the vehicle, that tries to locate just on the trajectory, can be tuned along side the vehicle vertical axis. This parameter mpt.kinematics.optimization center offset is defined as the signed length from the back-wheel center to the optimization center. Some examples are shown in the following figure, and it is shown that the trajectory of vehicle shape differs according to the optimization center even if the reference trajectory (green one) is the same.

mpt_optimization_offset

More details can be seen here.

applyInputVelocity

Velocity is assigned in the optimized trajectory from the velocity in the behavior path. The shapes of the optimized trajectory and the path are different, therefore the each nearest trajectory point to the path is searched and the velocity is interpolated with zero-order hold.

insertZeroVelocityOutsideDrivableArea

Optimized trajectory is too short for velocity planning, therefore extend the trajectory by concatenating the optimized trajectory and the behavior path considering drivability. Generated trajectory is checked if it is inside the drivable area or not, and if outside drivable area, output a trajectory inside drivable area with the behavior path or the previously generated trajectory.

As described above, the behavior path is separated into two paths: one is for optimization and the other is the remain. The first path becomes optimized trajectory, and the second path just is transformed to a trajectory. Then a trajectory inside the drivable area is calculated as follows.

  • If optimized trajectory is inside the drivable area, and the remained trajectory is inside/outside the drivable area,
    • the output trajectory will be just concatenation of those two trajectories.
    • In this case, we do not care if the remained trajectory is inside or outside the drivable area since generally it is outside the drivable area (especially in a narrow road), but we want to pass a trajectory as long as possible to the latter module.
  • If optimized trajectory is outside the drivable area, and the remained trajectory is inside/outside the drivable area,
    • and if the previously generated trajectory is memorized,
      • the output trajectory will be the previously generated trajectory, where zero velocity is inserted to the point firstly going outside the drivable area.
    • and if the previously generated trajectory is not memorized,
      • the output trajectory will be a part of trajectory just transformed from the behavior path, where zero velocity is inserted to the point firstly going outside the drivable area.

Optimization failure is dealt with the same as if the optimized trajectory is outside the drivable area. The output trajectory is memorized as a previously generated trajectory for the next cycle.

Rationale In the current design, since there are some modelling errors, the constraints are considered to be soft constraints. Therefore, we have to make sure that the optimized trajectory is inside the drivable area or not after optimization.

Limitation

  • Computation cost is sometimes high.
  • Because of the approximation such as linearization, some narrow roads cannot be run by the planner.
  • Roles of planning for behavior_path_planner and obstacle_avoidance_planner are not decided clearly. Both can avoid obstacles.

Comparison to other methods

Trajectory planning problem that satisfies kinematically-feasibility and collision-free has two main characteristics that makes hard to be solved: one is non-convex and the other is high dimension. Based on the characteristics, we investigate pros/cons of the typical planning methods: optimization-based, sampling-based, and learning-based method.

Optimization-based method

  • pros: comparatively fast against high dimension by leveraging the gradient descent
  • cons: often converge to the local minima in the non-convex problem

Sampling-based method

  • pros: realize global optimization
  • cons: high computation cost especially in the complex case

Learning-based method

  • under research yet

Based on these pros/cons, we chose the optimization-based planner first. Although it has a cons to converge to the local minima, it can get a good solution by the preprocessing to approximate the problem to convex that almost equals to the original non-convex problem.

How to Tune Parameters

Drivability in narrow roads

  • modify mpt.clearance.soft_clearance_from_road
    • This parameter describes how much margin to make between the trajectory and road boundaries.
    • Due to the model error for optimization, the constraint such as collision-free is not fully met.
      • By making this parameter larger, the is for narrow-road driving may be resolved. 12180
  • modify mpt.kinematics.optimization_center_offset

    • The point on the vehicle, offset forward with this parameter from the base link` tries to follow the reference path.
  • change or tune the method to approximate footprints with a set of circles.
    • See here
    • Tuning means changing the ratio of circle’s radius.

Computation time

  • under construction

Robustness

  • Check if the trajectory before or after MPT is not robust
    • if the trajectory before MPT is not robust
    • if the trajectory after MPT is not robust
      • make mpt.weight.steer_input_weight or mpt.weight.steer_rate_weight larger, which are stability of steering wheel along the trajectory.

Other options

  • option.enable_skip_optimization skips MPT optimization.
  • option.enable_calculation_time_info enables showing each calculation time for functions and total calculation time on the terminal.
  • option.enable_outside_drivable_area_stop enables stopping just before the generated trajectory point will be outside the drivable area.

How To Debug

How to debug can be seen here.

CHANGELOG
No CHANGELOG found.

Wiki Tutorials

This package does not provide any links to tutorials in it's rosindex metadata. You can check on the ROS Wiki Tutorials page for the package.

Launch files

Messages

No message files found.

Services

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Plugins

No plugins found.

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