When I use smac_planner and nav2_regulation_pure_pursuit_controlle in Ackerman, I still can't reverse the car [closed]

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A few days ago, I posted my problem in github and kept updating and debugging. Until now, I still can't realize the function of Ackerman robot reversing in navigation2. Besides setting the param file, is there any setting for the bt file?

Currently the parameter file I am using:Looking forward to a friend who can raise the error of my use of parameters, or other operational problems.

I use Ubuntu20.04-galactic version. ERROR when running: controller_server: ros__parameters: use_sim_time: True controller_frequency: 20.0 min_x_velocity_threshold: 0.001 min_y_velocity_threshold: 0.5 min_theta_velocity_threshold: 0.001 progress_checker_plugin: "progress_checker" goal_checker_plugins: "goal_checker" controller_plugins: ["FollowPath"]

    progress_checker:
      plugin: "nav2_controller::SimpleProgressChecker"
      required_movement_radius: 0.5
      movement_time_allowance: 10.0
    goal_checker:
      plugin: "nav2_controller::SimpleGoalChecker"
      xy_goal_tolerance: 0.25
      yaw_goal_tolerance: 0.25
      stateful: True
    FollowPath:
      plugin: "nav2_regulated_pure_pursuit_controller::RegulatedPurePursuitController"
      desired_linear_vel: 0.5
      lookahead_dist: 0.6
      min_lookahead_dist: 0.3
      max_lookahead_dist: 0.9
      lookahead_time: 1.5
      rotate_to_heading_angular_vel: 1.8
      transform_tolerance: 0.1
      use_velocity_scaled_lookahead_dist: false
      min_approach_linear_velocity: 0.05
      use_approach_linear_velocity_scaling: true
      max_allowed_time_to_collision_up_to_carrot: 1.0
      use_regulated_linear_velocity_scaling: true
      use_cost_regulated_linear_velocity_scaling: false
      regulated_linear_scaling_min_radius: 0.9
      regulated_linear_scaling_min_speed: 0.25
      use_rotate_to_heading: true
      rotate_to_heading_min_angle: 0.785
      max_angular_accel: 3.2
      max_robot_pose_search_dist: 10.0
      use_interpolation: false
      cost_scaling_dist: 0.3
      cost_scaling_gain: 1.0
      inflation_cost_scaling_factor: 3.0
      allow_reversing: True

controller_server_rclcpp_node:
  ros__parameters:
    use_sim_time: False


planner_server:
  ros__parameters:
    planner_plugins: ["GridBased"]
    use_sim_time: True

    GridBased:
      plugin: "nav2_smac_planner/SmacPlannerHybrid"
      downsample_costmap: false           # whether or not to downsample the map
      downsampling_factor: 1              # multiplier for the resolution of the costmap layer (e.g. 2 on a 5cm costmap would be 10cm)
      allow_unknown: true                 # allow traveling in unknown space
      max_iterations: 1000000             # maximum total iterations to search for before failing (in case unreachable), set to -1 to disable
      max_planning_time: 5.0              # max time in s for planner to plan, smooth
      motion_model_for_search: "Redds-Shepp"    # Hybrid-A* Dubin, Redds-Shepp
      angle_quantization_bins: 72         # Number of angle bins for search
      analytic_expansion_ratio: 3.5       # The ratio to attempt analytic expansions during search for final approach.
      analytic_expansion_max_length: 3.0  # For Hybrid/Lattice nodes: The maximum length of the analytic expansion to be considered valid to prevent unsafe shortcutting
      minimum_turning_radius: 0.40        # minimum turning radius in m of path / vehicle
      reverse_penalty: 2.0                # Penalty to apply if motion is reversing, must be => 1
      change_penalty: 0.0                 # Penalty to apply if motion is changing directions (L to R), must be >= 0
      non_straight_penalty: 1.2           # Penalty to apply if motion is non-straight, must be => 1
      cost_penalty: 2.0                   # Penalty to apply to higher cost areas when adding into the obstacle map dynamic programming distance expansion heuristic. This drives the robot more towards the center of passages. A value between 1.3 - 3.5 is reasonable.
      retrospective_penalty: 0.015
      lookup_table_size: 20.0             # Size of the dubin/reeds-sheep distance window to cache, in meters.
      cache_obstacle_heuristic: false     # Cache the obstacle map dynamic programming distance expansion heuristic between subsiquent replannings of the same goal location. Dramatically speeds up replanning performance (40x) if costmap is largely static.
      smooth_path: True                   # If true, does a simple and quick smoothing post-processing to the path

      smoother:
        max_iterations: 1000
        w_smooth: 0.3
        w_data: 0.2
        tolerance: 1e-10
        do_refinement: true

planner_server_rclcpp_node:
  ros__parameters:
    use_sim_time: False

recoveries_server:
  ros__parameters:
    costmap_topic: local_costmap/costmap_raw
    footprint_topic ...