Support for ackermann robots and propose a solution of zero linear velocity problem

[CihatAltiparmak]

Experimental ackermann config

amcl:
  ros__parameters:
    alpha1: 0.2
    alpha2: 0.2
    alpha3: 0.2
    alpha4: 0.2
    alpha5: 0.2
    base_frame_id: "base_footprint"
    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: 100.0
    laser_min_range: -1.0
    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: "nav2_amcl::DifferentialMotionModel"
    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

bt_navigator:
  ros__parameters:
    global_frame: map
    robot_base_frame: base_link
    odom_topic: odom
    bt_loop_duration: 10
    default_server_timeout: 20
    wait_for_service_timeout: 1000
    action_server_result_timeout: 900.0
    navigators: ["navigate_to_pose", "navigate_through_poses"]
    navigate_to_pose:
      plugin: "nav2_bt_navigator::NavigateToPoseNavigator"
    navigate_through_poses:
      plugin: "nav2_bt_navigator::NavigateThroughPosesNavigator"
    # 'default_nav_through_poses_bt_xml' and 'default_nav_to_pose_bt_xml' are use defaults:
    # nav2_bt_navigator/navigate_to_pose_w_replanning_and_recovery.xml
    # nav2_bt_navigator/navigate_through_poses_w_replanning_and_recovery.xml
    # They can be set here or via a RewrittenYaml remap from a parent launch file to Nav2.

    # plugin_lib_names is used to add custom BT plugins to the executor (vector of strings).
    # Built-in plugins are added automatically
    # plugin_lib_names: []

    error_code_names:
      - compute_path_error_code
      - follow_path_error_code

controller_server:
  ros__parameters:
    controller_frequency: 20.0
    costmap_update_timeout: 0.30
    min_x_velocity_threshold: 0.001
    min_y_velocity_threshold: 0.5
    min_theta_velocity_threshold: 0.001
    failure_tolerance: 0.3
    progress_checker_plugins: ["progress_checker"]
    goal_checker_plugins: ["general_goal_checker"] # "precise_goal_checker"
    controller_plugins: ["FollowPath"]
    use_realtime_priority: false

    # Progress checker parameters
    progress_checker:
      plugin: "nav2_controller::SimpleProgressChecker"
      required_movement_radius: 0.5
      movement_time_allowance: 10.0
    # Goal checker parameters
    #precise_goal_checker:
    #  plugin: "nav2_controller::SimpleGoalChecker"
    #  xy_goal_tolerance: 0.25
    #  yaw_goal_tolerance: 0.25
    #  stateful: True
    general_goal_checker:
      stateful: True
      plugin: "nav2_controller::SimpleGoalChecker"
      xy_goal_tolerance: 0.25
      yaw_goal_tolerance: 0.25
    FollowPath:
      plugin: "nav2_frenet_ilqr_controller::FrenetILQRController"
      time_discretization: 0.05
      frenet_trajectory_planner:
        min_lateral_distance: -1.0
        max_lateral_distance: 1.0
        step_lateral_distance: 0.1
        min_longtitutal_velocity: -0.1
        max_longtitutal_velocity: 0.5
        step_longtitutal_velocity: 0.05
        time_interval: 2.0
        max_state_in_trajectory: 40
      ilqr_trajectory_tracker:
        iteration_number: 20
        alpha: 1.0
      cost_checker_plugins: ["LateralDistanceCostChecker", "LongtitutalVelocityCostChecker"]
      LateralDistanceCostChecker:
        plugin: "nav2_frenet_ilqr_controller::costs::LateralDistanceCost"
        K_lateral_distance: 10.0
      LongtitutalVelocityCostChecker:
        plugin: "nav2_frenet_ilqr_controller::costs::LongtitutalVelocityCost"
        K_longtitutal_velocity: 5.0
        desired_velocity: 0.5

      # policy_plugins: ["ObstaclePolicy", "CurvaturePolicy"]
      policy_plugins: ["ObstaclePolicy"]
      ObstaclePolicy:
        plugin: "nav2_frenet_ilqr_controller::policies::ObstaclePolicy"
      CurvaturePolicy:
        plugin: "nav2_frenet_ilqr_controller::policies::CurvaturePolicy"

local_costmap:
  local_costmap:
    ros__parameters:
      update_frequency: 5.0
      publish_frequency: 2.0
      global_frame: odom
      robot_base_frame: base_link
      rolling_window: true
      width: 3
      height: 3
      resolution: 0.05
      robot_radius: 0.22
      plugins: ["voxel_layer", "inflation_layer"]
      inflation_layer:
        plugin: "nav2_costmap_2d::InflationLayer"
        cost_scaling_factor: 3.0
        inflation_radius: 0.70
      voxel_layer:
        plugin: "nav2_costmap_2d::VoxelLayer"
        enabled: True
        publish_voxel_map: True
        origin_z: 0.0
        z_resolution: 0.05
        z_voxels: 16
        max_obstacle_height: 2.0
        mark_threshold: 0
        observation_sources: scan
        scan:
          # A relative topic will be appended to the parent of the local_costmap namespace.
          # For example:
          #   * User chosen namespace is `tb4`.
          #   * User chosen topic is `scan`.
          #   * Topic will be remapped to `/tb4/scan` without `local_costmap`.
          #   * Use global topic `/scan` if you do not wish the node namespace to apply
          topic: scan
          max_obstacle_height: 2.0
          clearing: True
          marking: True
          data_type: "LaserScan"
          raytrace_max_range: 3.0
          raytrace_min_range: 0.0
          obstacle_max_range: 2.5
          obstacle_min_range: 0.0
      static_layer:
        plugin: "nav2_costmap_2d::StaticLayer"
        map_subscribe_transient_local: True
      always_send_full_costmap: True

global_costmap:
  global_costmap:
    ros__parameters:
      update_frequency: 1.0
      publish_frequency: 1.0
      global_frame: map
      robot_base_frame: base_link
      robot_radius: 0.22
      resolution: 0.05
      track_unknown_space: true
      plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
      obstacle_layer:
        plugin: "nav2_costmap_2d::ObstacleLayer"
        enabled: True
        observation_sources: scan
        scan:
          # A relative topic will be appended to the parent of the global_costmap namespace.
          # For example:
          #   * User chosen namespace is `tb4`.
          #   * User chosen topic is `scan`.
          #   * Topic will be remapped to `/tb4/scan` without `global_costmap`.
          #   * Use global topic `/scan` if you do not wish the node namespace to apply
          topic: scan
          max_obstacle_height: 2.0
          clearing: True
          marking: True
          data_type: "LaserScan"
          raytrace_max_range: 3.0
          raytrace_min_range: 0.0
          obstacle_max_range: 2.5
          obstacle_min_range: 0.0
      static_layer:
        plugin: "nav2_costmap_2d::StaticLayer"
        map_subscribe_transient_local: True
      inflation_layer:
        plugin: "nav2_costmap_2d::InflationLayer"
        cost_scaling_factor: 3.0
        inflation_radius: 0.7
      always_send_full_costmap: True

# The yaml_filename does not need to be specified since it going to be set by defaults in launch.
# If you'd rather set it in the yaml, remove the default "map" value in the tb3_simulation_launch.py
# file & provide full path to map below. If CLI map configuration or launch default is provided, that will be used.
# map_server:
#   ros__parameters:
#     yaml_filename: ""

map_saver:
  ros__parameters:
    save_map_timeout: 5.0
    free_thresh_default: 0.25
    occupied_thresh_default: 0.65
    map_subscribe_transient_local: True

planner_server:
  ros__parameters:
    planner_plugins: ["GridBased"]

    GridBased:
      plugin: "nav2_smac_planner::SmacPlannerHybrid"
      tolerance: 0.5                      # tolerance for planning if unable to reach exact pose, in meters
      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: false                # 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_on_approach_iterations: 1000    # maximum number of iterations to attempt to reach goal once in tolerance
      terminal_checking_interval: 5000     # number of iterations between checking if the goal has been cancelled or planner timed out
      max_planning_time: 3.5              # max time in s for planner to plan, smooth, and upsample. Will scale maximum smoothing and upsampling times based on remaining time after planning.
      motion_model_for_search: "DUBIN"    # For Hybrid Dubin, Reeds-Shepp
      cost_travel_multiplier: 2.0         # For 2D: Cost multiplier to apply to search to steer away from high cost areas. Larger values will place in the center of aisles more exactly (if non-`FREE` cost potential field exists) but take slightly longer to compute. To optimize for speed, a value of 1.0 is reasonable. A reasonable tradeoff value is 2.0. A value of 0.0 effective disables steering away from obstacles and acts like a naive binary search A*.
      angle_quantization_bins: 64         # For Hybrid nodes: Number of angle bins for search, must be 1 for 2D node (no angle search)
      analytic_expansion_ratio: 3.5       # For Hybrid/Lattice nodes: 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 (in meters). This should be scaled with minimum turning radius and be no less than 4-5x the minimum radius
      analytic_expansion_max_cost: 200.0   # For Hybrid/Lattice nodes: The maximum single cost for any part of an analytic expansion to contain and be valid (except when necessary on approach to goal)
      analytic_expansion_max_cost_override: false # For Hybrid/Lattice nodes: Whether or not to override the maximum cost setting if within critical distance to goal (ie probably required). If expansion is within 2*pi*min_r of the goal, then it will override the max cost if ``false``.
      minimum_turning_radius: 0.40        # For Hybrid/Lattice nodes: minimum turning radius in m of path / vehicle
      reverse_penalty: 2.1                # For Reeds-Shepp model: penalty to apply if motion is reversing, must be => 1
      change_penalty: 0.0                 # For Hybrid nodes: penalty to apply if motion is changing directions, must be >= 0
      non_straight_penalty: 1.20          # For Hybrid nodes: penalty to apply if motion is non-straight, must be => 1
      cost_penalty: 2.0                   # For Hybrid nodes: 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: 0.025        # For Hybrid/Lattice nodes: penalty to prefer later maneuvers before earlier along the path. Saves search time since earlier nodes are not expanded until it is necessary. Must be >= 0.0 and <= 1.0
      rotation_penalty: 5.0               # For Lattice node: Penalty to apply only to pure rotate in place commands when using minimum control sets containing rotate in place primitives. This should always be set sufficiently high to weight against this action unless strictly necessary for obstacle avoidance or there may be frequent discontinuities in the plan where it requests the robot to rotate in place to short-cut an otherwise smooth path for marginal path distance savings.
      lookup_table_size: 20.0               # For Hybrid nodes: Size of the dubin/reeds-sheep distance window to cache, in meters.
      cache_obstacle_heuristic: True      # For Hybrid nodes: Cache the obstacle map dynamic programming distance expansion heuristic between subsequent replannings of the same goal location. Dramatically speeds up replanning performance (40x) if costmap is largely static.
      allow_reverse_expansion: False      # For Lattice nodes: Whether to expand state lattice graph in forward primitives or reverse as well, will double the branching factor at each step.
      smooth_path: True                   # For Lattice/Hybrid nodes: Whether or not to smooth the path, always true for 2D nodes.
      debug_visualizations: True                # For Hybrid/Lattice nodes: Whether to publish expansions on the /expansions topic as an array of poses (the orientation has no meaning) and the path's footprints on the /planned_footprints topic. WARNING: heavy to compute and to display, for debug only as it degrades the performance.
      smoother:
        max_iterations: 1000
        w_smooth: 0.3
        w_data: 0.2
        tolerance: 1.0e-10
        do_refinement: true               # Whether to recursively run the smoother 3 times on the results from prior runs to refine the results further

smoother_server:
  ros__parameters:
    smoother_plugins: ["simple_smoother"]
    simple_smoother:
      plugin: "nav2_smoother::SimpleSmoother"
      tolerance: 1.0e-10
      max_its: 1000
      do_refinement: True

behavior_server:
  ros__parameters:
    local_costmap_topic: local_costmap/costmap_raw
    global_costmap_topic: global_costmap/costmap_raw
    local_footprint_topic: local_costmap/published_footprint
    global_footprint_topic: global_costmap/published_footprint
    cycle_frequency: 10.0
    behavior_plugins: ["spin", "backup", "drive_on_heading", "assisted_teleop", "wait"]
    spin:
      plugin: "nav2_behaviors::Spin"
    backup:
      plugin: "nav2_behaviors::BackUp"
      acceleration_limit: 2.5
      deceleration_limit: -2.5
      minimum_speed: 0.10
    drive_on_heading:
      plugin: "nav2_behaviors::DriveOnHeading"
      acceleration_limit: 2.5
      deceleration_limit: -2.5
      minimum_speed: 0.10
    wait:
      plugin: "nav2_behaviors::Wait"
    assisted_teleop:
      plugin: "nav2_behaviors::AssistedTeleop"
    local_frame: odom
    global_frame: map
    robot_base_frame: base_link
    transform_tolerance: 0.1
    simulate_ahead_time: 2.0
    max_rotational_vel: 1.0
    min_rotational_vel: 0.4
    rotational_acc_lim: 3.2

waypoint_follower:
  ros__parameters:
    loop_rate: 20
    stop_on_failure: false
    action_server_result_timeout: 900.0
    waypoint_task_executor_plugin: "wait_at_waypoint"
    wait_at_waypoint:
      plugin: "nav2_waypoint_follower::WaitAtWaypoint"
      enabled: True
      waypoint_pause_duration: 200

velocity_smoother:
  ros__parameters:
    smoothing_frequency: 20.0
    scale_velocities: False
    feedback: "OPEN_LOOP"
    max_velocity: [0.5, 0.0, 2.0]
    min_velocity: [-0.5, 0.0, -2.0]
    max_accel: [2.5, 0.0, 3.2]
    max_decel: [-2.5, 0.0, -3.2]
    odom_topic: "odom"
    odom_duration: 0.1
    deadband_velocity: [0.0, 0.0, 0.0]
    velocity_timeout: 1.0

collision_monitor:
  ros__parameters:
    base_frame_id: "base_footprint"
    odom_frame_id: "odom"
    cmd_vel_in_topic: "cmd_vel_smoothed"
    cmd_vel_out_topic: "cmd_vel"
    state_topic: "collision_monitor_state"
    transform_tolerance: 0.2
    source_timeout: 1.0
    base_shift_correction: True
    stop_pub_timeout: 2.0
    # Polygons represent zone around the robot for "stop", "slowdown" and "limit" action types,
    # and robot footprint for "approach" action type.
    polygons: ["FootprintApproach"]
    FootprintApproach:
      type: "polygon"
      action_type: "approach"
      footprint_topic: "local_costmap/published_footprint"
      time_before_collision: 1.2
      simulation_time_step: 0.1
      min_points: 6
      visualize: False
      enabled: True
    observation_sources: ["scan"]
    scan:
      type: "scan"
      topic: "scan"
      min_height: 0.15
      max_height: 2.0
      enabled: True

docking_server:
  ros__parameters:
    controller_frequency: 50.0
    initial_perception_timeout: 5.0
    wait_charge_timeout: 5.0
    dock_approach_timeout: 30.0
    undock_linear_tolerance: 0.05
    undock_angular_tolerance: 0.1
    max_retries: 3
    base_frame: "base_link"
    fixed_frame: "odom"
    dock_backwards: false
    dock_prestaging_tolerance: 0.5

    # Types of docks
    dock_plugins: ['simple_charging_dock']
    simple_charging_dock:
      plugin: 'opennav_docking::SimpleChargingDock'
      docking_threshold: 0.05
      staging_x_offset: -0.7
      use_external_detection_pose: true
      use_battery_status: false # true
      use_stall_detection: false # true

      external_detection_timeout: 1.0
      external_detection_translation_x: -0.18
      external_detection_translation_y: 0.0
      external_detection_rotation_roll: -1.57
      external_detection_rotation_pitch: -1.57
      external_detection_rotation_yaw: 0.0
      filter_coef: 0.1

    # Dock instances
    # The following example illustrates configuring dock instances.
    # docks: ['home_dock']  # Input your docks here
    # home_dock:
    #   type: 'simple_charging_dock'
    #   frame: map
    #   pose: [0.0, 0.0, 0.0]

    controller:
      k_phi: 3.0
      k_delta: 2.0
      v_linear_min: 0.15
      v_linear_max: 0.15
      use_collision_detection: true
      costmap_topic: "/local_costmap/costmap_raw"
      footprint_topic: "/local_costmap/published_footprint"
      transform_tolerance: 0.1
      projection_time: 5.0
      simulation_step: 0.1
      dock_collision_threshold: 0.3

loopback_simulator:
  ros__parameters:
    base_frame_id: "base_footprint"
    odom_frame_id: "odom"
    map_frame_id: "map"
    scan_frame_id: "base_scan"  # tb4_loopback_simulator.launch.py remaps to 'rplidar_link'
    update_duration: 0.02