1. Costmaps

• Cost map is a grid in which every cell gets assigned a value (cost) determining distance to obstacle, where higher value means closer distance.
• Using this map, the robot plans the path in such a way that it avoids obtsacles by creating a trajectory with lowest cost.
• There are 2 costmaps, one for local planner which determines obtsacles near the robot and the other one for global planner to plan a global path from the start point to the goal with keeping the obstacles in mind.

1.1 Local Costmap

• These are the parameters used by a local cost map in nav2_parma.yaml in dir /ebot_nav2/params/.

local_costmap:
  local_costmap:
    ros__parameters:
      update_frequency: 5.0
      publish_frequency: 2.0
      global_frame: odom
      robot_base_frame: "base_footprint"
      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

1.2 Global Costmap

• These are the parameters used by a global cost map in nav2_parma.yaml in dir /ebot_nav2/params/.

global_costmap:
  global_costmap:
    ros__parameters:
      update_frequency: 1.0
      publish_frequency: 1.0
      global_frame: map
      robot_base_frame: "base_footprint"
      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

2 Planner

• There are 2 types of planners in the Navigation stack:
  1. Local Planner - Global planner plans the path from the start to the end goal.
  2. Global Planner - Local planner is used to avoid obstacles and get the robot back to the global path after avoiding obstacles.

2.1 Local Path Planner

• The purpose of local planner is to find a suitable local plan at every instance.
• There are various local planners that are used. We will be using the dwa local planner.
• Using a map, the planner creates a kinematic trajectory for the robot to get from a start to a goal location.
• Along the way, the planner creates, at least locally around the robot, a value function, represented as a grid map.