Navigation2专题六：设置机器人的Footprint

Posted by Mu Wenfeng on 2021-11-11

footprint（足迹）简介

  • footprint（足迹）是机器人投影到地面的轮廓，Nav2根据这个轮廓在规划路径时躲避障碍物。这个Nav2任务中使用的算法会确保机器人在计算路径或做规划时不会与代价地图中的障碍物发生碰撞。
  • 在全局和局部代价地图中，足迹是通过参数footprint或者robot_radius来设置的。参数footprint定义的值是一个二维平面点的有序数组，它们以base_link坐标系为原点来确定机器人的足迹，从数组中的第一个点到最后一个点连接起来就能形成一个闭合的足迹的形状；作为替代方案，robot_radius参数则是以base_link为中心形成了一个圆形的轮廓线。如果在配置中两个参数都被设置了，那么优先使用footprint参数。
  • 对于全局代价地图（global costmap）中的足迹，选择robot_radius（圆形）还是footprint（多边形）参数取决于机器人本身、他所处的环境以及所采用的规划算法。
    • 非圆形的机器人也可能使用圆形足迹。比如，路径规划算法NavFn会假定机器人是圆形的，因为它仅仅检测每个网格单元的碰撞，没有必要去精确的勾勒机器人的形状。
    • Smac Planner’s Hybrid-A* 则是尽可能对机器人的多边形足迹进行碰撞检测。
    • 一个小型RC汽车大小的机器人在仓库中漫游，由于它相对仓库来说非常小，用横截面的半径来近似它是一个非常好的优化。
  • 对于局部代价地图（local costmap）中的足迹，一个典型的做法是为非圆形的机器人设置footprint(多边形)参数。
    • 如果没有足够的计算资源的话，则不推荐使用footprint参数。
    • 当机器人相对于它的环境非常微小时，精确的避障则没有必要。

配置机器人的足迹

我们以sam_bot为对象，footprint参数用于局部代价地图，而robot_radius参数用于全局代价地图。我们将修改Nav2默认配置文件中全局和局部代价地图的的footprint参数。

  • 在sam_bot_description工程的config目录下，创建名为nav2_params.yaml的文件，将以下内容拷贝到这个文件中：

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    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_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: "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: /odom
        bt_loop_duration: 10
        default_server_timeout: 20
        enable_groot_monitoring: True
        groot_zmq_publisher_port: 1666
        groot_zmq_server_port: 1667
        # '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:
        - nav2_compute_path_to_pose_action_bt_node
        - nav2_compute_path_through_poses_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
        - nav2_single_trigger_bt_node
        - nav2_is_battery_low_condition_bt_node
        - nav2_navigate_through_poses_action_bt_node
        - nav2_navigate_to_pose_action_bt_node
        - nav2_remove_passed_goals_action_bt_node
        - nav2_planner_selector_bt_node
        - nav2_controller_selector_bt_node
        - nav2_goal_checker_selector_bt_node

    bt_navigator_rclcpp_node:
      ros__parameters:
        use_sim_time: True

    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
        failure_tolerance: 0.3
        progress_checker_plugin: "progress_checker"
        goal_checker_plugins: ["general_goal_checker"] # "precise_goal_checker"
        controller_plugins: ["FollowPath"]

        # 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
        # DWB parameters
        FollowPath:
          plugin: "dwb_core::DWBLocalPlanner"
          debug_trajectory_details: True
          min_vel_x: 0.0
          min_vel_y: 0.0
          max_vel_x: 0.26
          max_vel_y: 0.0
          max_vel_theta: 1.0
          min_speed_xy: 0.0
          max_speed_xy: 0.26
          min_speed_theta: 0.0
          # Add high threshold velocity for turtlebot 3 issue.
          # https://github.com/ROBOTIS-GIT/turtlebot3_simulations/issues/75
          acc_lim_x: 2.5
          acc_lim_y: 0.0
          acc_lim_theta: 3.2
          decel_lim_x: -2.5
          decel_lim_y: 0.0
          decel_lim_theta: -3.2
          vx_samples: 20
          vy_samples: 5
          vtheta_samples: 20
          sim_time: 1.7
          linear_granularity: 0.05
          angular_granularity: 0.025
          transform_tolerance: 0.2
          xy_goal_tolerance: 0.25
          trans_stopped_velocity: 0.25
          short_circuit_trajectory_evaluation: True
          stateful: True
          critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
          BaseObstacle.scale: 0.02
          PathAlign.scale: 32.0
          PathAlign.forward_point_distance: 0.1
          GoalAlign.scale: 24.0
          GoalAlign.forward_point_distance: 0.1
          PathDist.scale: 32.0
          GoalDist.scale: 24.0
          RotateToGoal.scale: 32.0
          RotateToGoal.slowing_factor: 5.0
          RotateToGoal.lookahead_time: -1.0
          
    controller_server_rclcpp_node:
      ros__parameters:
        use_sim_time: True

    local_costmap:
      local_costmap:
        ros__parameters:
          update_frequency: 5.0
          publish_frequency: 2.0
          global_frame: odom
          robot_base_frame: base_link
          use_sim_time: True
          rolling_window: true
          width: 3
          height: 3
          resolution: 0.05
          footprint: "[ [0.21, 0.195], [0.21, -0.195], [-0.21, -0.195], [-0.21, 0.195] ]"
          plugins: ["voxel_layer", "inflation_layer"]
          inflation_layer:
            plugin: "nav2_costmap_2d::InflationLayer"
            cost_scaling_factor: 3.0
            inflation_radius: 0.55
          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:
              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:
            map_subscribe_transient_local: True
          always_send_full_costmap: True
      local_costmap_client:
        ros__parameters:
          use_sim_time: True
      local_costmap_rclcpp_node:
        ros__parameters:
          use_sim_time: True

    global_costmap:
      global_costmap:
        ros__parameters:
          update_frequency: 1.0
          publish_frequency: 1.0
          global_frame: map
          robot_base_frame: base_link
          use_sim_time: True
          robot_radius: 0.3
          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:
              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: 0.55
          always_send_full_costmap: True
      global_costmap_client:
        ros__parameters:
          use_sim_time: True
      global_costmap_rclcpp_node:
        ros__parameters:
          use_sim_time: True

    map_server:
      ros__parameters:
        use_sim_time: True
        yaml_filename: "turtlebot3_world.yaml"

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

    planner_server:
      ros__parameters:
        expected_planner_frequency: 20.0
        use_sim_time: True
        planner_plugins: ["GridBased"]
        GridBased:
          plugin: "nav2_navfn_planner/NavfnPlanner"
          tolerance: 0.5
          use_astar: false
          allow_unknown: true

    planner_server_rclcpp_node:
      ros__parameters:
        use_sim_time: True

    recoveries_server:
      ros__parameters:
        costmap_topic: local_costmap/costmap_raw
        footprint_topic: local_costmap/published_footprint
        cycle_frequency: 10.0
        recovery_plugins: ["spin", "backup", "wait"]
        spin:
          plugin: "nav2_recoveries/Spin"
        backup:
          plugin: "nav2_recoveries/BackUp"
        wait:
          plugin: "nav2_recoveries/Wait"
        global_frame: odom
        robot_base_frame: base_link
        transform_timeout: 0.1
        use_sim_time: true
        simulate_ahead_time: 2.0
        max_rotational_vel: 1.0
        min_rotational_vel: 0.4
        rotational_acc_lim: 3.2

    robot_state_publisher:
      ros__parameters:
        use_sim_time: True

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

    这些内容都是来自Nav2的默认配置文件，但是修改了local_costmap的footprint和global_costmap的robot_radius参数以适配sam_bot的形状。

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    resolution: 0.05
    footprint: "[ [0.21, 0.195], [0.21, -0.195], [-0.21, -0.195], [-0.21, 0.195] ]"
    plugins: ["voxel_layer", "inflation_layer"]
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    use_sim_time: True
    robot_radius: 0.3
    resolution: 0.05

编译与运行

  • 重新编译整个工程，然后运行display.launch.py脚本，用以启动robot state publisher，在Gazebo中孵化sam_bot，在Rviz中可视化sam_bot和它的足迹。

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    colcon build
    . install/setup.bash
    ros2 launch sam_bot_description display.launch.py

  • 接下来，为了简单起见，使用tf2_ros中的static_transform_publiser来发布map=>odom的坐标变换。

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    ros2 run tf2_ros static_transform_publisher 0 0 0 0 0 0 map odom

  • 最后，启动Nav2，并使用nav_params.yaml

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    ros2 launch nav2_bringup navigation_launch.py params_file:=<full/path/to/config/nav2_params.yaml>

    params_file后面为文件绝对路径比如ros2 launch nav2_bringup navigation_launch.py params_file:=/home/ubuntu-ros2/robot_sim/src/sam_bot_description/config/nav2_params.yaml

  • 在Rviz中可视化局部代价地图的足迹

    Rviz左侧窗口中点击add按钮，选中By topic标签，选择/local_costmap/published_footprint话题下的Polygon

    ../../_images/add_topic_local_costmap.png

    image-20211111163331001

  • 在Rviz中可视化全局代价地图中的足迹

    Rviz左侧窗口中电机add，选中By topic标签，选择/global_costmap/published_footprint话题下的Polygon

    ../../_images/add_topic_global_costmap.png

​ image-20211111163847503




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