planner_server:
  ros__parameters:
    expected_planner_frequency: 1.0
    use_sim_time: True
    planner_plugins: ["GridBased"]
    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)
      tolerance: 0.25 # dist-to-goal heuristic cost (distance) for valid tolerance endpoints if exact goal cannot be found.
      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_on_approach_iterations: 1000 # Maximum number of iterations after within tolerances to continue to try to find exact solution
      max_planning_time: 5.0 # max time in s for planner to plan, smooth
      motion_model_for_search: "REEDS_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: 1.5 # 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: 1.0 # 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: 3.0 # 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.
      debug_visualizations: false # For Hybrid 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.
      use_quadratic_cost_penalty: False
      downsample_obstacle_heuristic: True
      allow_primitive_interpolation: False
      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: 1.0e-10
        do_refinement: true
        refinement_num: 2