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Nav2 global and local planner conflict

asked 2023-06-18 14:44:55 -0500

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Hello, I'm working with an Ackermann vehicle and using the TEB controller as the local planner (I built it for Foxy) After some tuning, I'm finally getting the vehicle to navigate kind of properly. However, the local planner's path is always different than the global planner's path. This is expected, I assume (but correct me if I'm wrong), but in some complex maneuvers, for example, when involving going in reverse, the path becomes really different and I think the global and local planners are kinda fighting each other. In most cases, the vehicle will make it to the goal but other times it just goes back and forth between the 2 paths and after a while it fails.

Below is an example of a case where the vehicle needs to reverse first to get to the goal. The good thing is that the paths look symmetrical, which means the 2 planners are using similar kinematic constraints. But, regardless, how can I solve this "conflict"?
image description

Here are my Nav2 params:

amcl:
  ros__parameters:
    use_sim_time: True
    alpha1: 0.2
    alpha2: 0.2
    alpha3: 0.2
    alpha4: 0.2
    alpha5: 0.2
    base_frame_id: "base_link"
    beam_skip_distance: 0.5
    beam_skip_error_threshold: 0.9
    beam_skip_threshold: 0.3
    do_beamskip: false
    global_frame_id: "map"
    lambda_short: 0.1
    laser_likelihood_max_dist: 2.0
    laser_max_range: 20.0
    laser_min_range: 0.1
    laser_model_type: "likelihood_field"
    max_beams: 60
    max_particles: 2000
    min_particles: 500
    odom_frame_id: "odom"
    pf_err: 0.05
    pf_z: 0.99
    recovery_alpha_fast: 0.0
    recovery_alpha_slow: 0.0
    resample_interval: 1
    robot_model_type: "differential"
    save_pose_rate: 0.5
    sigma_hit: 0.2
    tf_broadcast: true
    transform_tolerance: 1.0
    update_min_a: 0.2
    update_min_d: 0.25
    z_hit: 0.5
    z_max: 0.05
    z_rand: 0.5
    z_short: 0.05
    scan_topic: scan

amcl_map_client:
  ros__parameters:
    use_sim_time: True

amcl_rclcpp_node:
  ros__parameters:
    use_sim_time: True

bt_navigator:
  ros__parameters:
    use_sim_time: True
    global_frame: map
    robot_base_frame: base_link
    odom_topic: /odometry/filtered
    enable_groot_monitoring: True
    groot_zmq_publisher_port: 1666
    groot_zmq_server_port: 1667
    # default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
    default_bt_xml_filename: "navigate_w_replanning_and_round_robin_recovery"
    # default_bt_xml_filename: "follow_point.xml"
    plugin_lib_names:
    - nav2_compute_path_to_pose_action_bt_node
    - nav2_follow_path_action_bt_node
    - nav2_back_up_action_bt_node
    - nav2_spin_action_bt_node
    - nav2_wait_action_bt_node
    - nav2_clear_costmap_service_bt_node
    - nav2_is_stuck_condition_bt_node
    - nav2_goal_reached_condition_bt_node
    - nav2_goal_updated_condition_bt_node
    - nav2_initial_pose_received_condition_bt_node
    - nav2_reinitialize_global_localization_service_bt_node
    - nav2_rate_controller_bt_node
    - nav2_distance_controller_bt_node
    - nav2_speed_controller_bt_node
    - nav2_truncate_path_action_bt_node
    - nav2_goal_updater_node_bt_node
    - nav2_recovery_node_bt_node
    - nav2_pipeline_sequence_bt_node
    - nav2_round_robin_node_bt_node
    - nav2_transform_available_condition_bt_node
    - nav2_time_expired_condition_bt_node
    - nav2_distance_traveled_condition_bt_node

bt_navigator_rclcpp_node:
  ros__parameters:
    use_sim_time: True

controller_server:
  ros__parameters:
    use_sim_time: True
    controller_frequency: 2.0
    min_x_velocity_threshold: 0.005
    min_y_velocity_threshold: 0.005
    min_theta_velocity_threshold: 0.01
    failure_tolerance: 0.3
    odom_topic: /odometry/filtered
    progress_checker_plugin: "progress_checker"
    goal_checker_plugins: ["goal_checker"]
    controller_plugins: ["FollowPath"]

    progress_checker:
      plugin: "nav2_controller::SimpleProgressChecker"
      required_movement_radius: 0.05
      movement_time_allowance: 10.0
    goal_checker:
      plugin: "nav2_controller::SimpleGoalChecker"
      xy_goal_tolerance: 0.2 # 0.25
      yaw_goal_tolerance: 0.1 #0.25
      stateful: True
    # TEB parameters
    # http://wiki.ros.org/teb_local_planner
    FollowPath:
      plugin: "teb_local_planner::TebLocalPlannerROS"
      odom_topic: odom
      map_frame: map

      # footprint_model.type: circular
      # footprint_model.radius: 2.0
      footprint_model.type: polygon
      footprint_model.vertices: "[ [1.54, 0.405], [1.54, -0.405], [-0.2, -0.405], [-0.2, 0.405] ]"

      min_turning_radius: 2.0
      wheelbase: 1.35
      cmd_angle_instead_rotvel: true # INVESTIGATE MORE!
      min_obstacle_dist: 1.0
      inflation_dist: 1.0
      costmap_converter_plugin: "costmap_converter::CostmapToPolygonsDBSMCCH"
      costmap_converter_spin_thread: True
      costmap_converter_rate: 15
      enable_homotopy_class_planning: True
      enable_multithreading: True
      optimization_verbose: False
      teb_autoresize: True
      min_samples: 3
      max_samples: 20
      max_global_plan_lookahead_dist: 5.0
      visualize_hc_graph: True
      max_vel_x: 0.75
      max_vel_x_backwards: 0.2
      max_vel_y: 0.0 # should be zero for non-holonomic robots
      acc_lim_y: 0.05
      max_vel_theta: 1.0
      acc_lim_x: 0.5
      acc_lim_theta: 0 ...
(more)
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answered 2023-06-19 14:15:30 -0500

You're not seeing "fighting" because there is no feedback between them. The global planner sends things to the local planner and it makes the judgement call about what to do.

Keep in mind TEB predates the Smac Planners by ... a decade or so, so it doesn't really know anything about the nature of the feasible path. Its used to working with outputs from NavFn which are infeasible and TEB's job is to make it feasible by those constraints itself -- so that's what its doing but ignoring Smac's requested maneuvers.

This isn't necessarily a problem unless you want it to exactly follow the Smac Planner's requested direction or TEB is oscillating so often that its not making progress. If you want the robot to always just follow the Smac Plan, you may want to instead just use the RPP controller which will just following the Smac plan exactly and there's no problem because its not a predictive controller that will try to deviate from the path in order to optimize for other criteria.

If you want that predictive behavior in general but not when talking about directional changes, I'd recommend using the MPPI controller that's been released which is a successor to TEB. This will do that for you for the most part currently and I'm working on some branches currently which will provide better enforce doing those path inversions at the same place as the Smac Planner requested very shortly (approx. by the end of the week).

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Asked: 2023-06-18 14:44:55 -0500

Seen: 352 times

Last updated: Jun 19 '23