Skip to content

Latest commit

 

History

History
30 lines (28 loc) · 3.2 KB

README.md

File metadata and controls

30 lines (28 loc) · 3.2 KB
Raw

tuw_global_planner

This module holds modified clone of the global planner used in ros navigation of https://github.com/ros-planning/navigation.git

global_planner

This pkg implements a voronoi planner to route a single vehicle along a generated voronoi graph. The voronoi graph is generated based on the received map message. Thanks goes to the ROS move_base comunity because this pkg holds a copy of the global_planner used in move_base https://github.com/ros-planning/navigation.git and extends it with a A-Star Planner with different Heuristics and a embedded Voronoi Graph Generator to replace the map with it.

parameters

  • allow_unknown (bool, default: true): Specifies whether or not to allow the planner to create plans that traverse unknown space. NOTE: if you are using a layered costmap_2d costmap with a voxel or obstacle layer, you must also set the track_unknown_space param for that layer to be true, or it will convert all your unknown space to free space (which planner will then happily go right through).
  • default_tolerance (double, default: 0.0): A tolerance on the goal point for the planner. The planner will attempt to create a plan that is as close to the specified goal as possible but no further than default_tolerance away.
  • visualize_potential (bool, default: false): Specifies whether or not to visualize the potential area computed via a PointCloud2.
  • use_dijkstra (bool, default: true): If true, use dijkstra's algorithm. Otherwise, A*.
  • use_quadratic (bool, default: true): If true, use the quadratic approximation of the potential. Otherwise, use a simpler calculation.
  • use_grid_path (bool, default: false): If true, create a path that follows the grid boundaries. Otherwise, use a gradient descent method.
  • old_navfn_behavior (bool, default: false): If for some reason, you want global_planner to exactly mirror the behavior of navfn, set this to true (and use the defaults for the other boolean parameters)
  • use_astar_tuw: If true the TUW Astar mode is activated and a heuristic can be selected.
  • use_voronoi: If true the given map is converted to a voronoi map and the TUW AStar algorithm is used to plan a path
  • astar_heuristics: 0 (no heuristic==dirjkstra), 1 (Manhatten), 2 (Diagonal), 3 (Euclidean distance)
  • heuristics_weight: With this parameter the weight of the heuristic can be adjusted (0 means dijkstra ... 1 means full heuristic)

tuw_waypoint_to_spline

converts a point array with chassis orientation into a spline functions with knots and control points

parameters

  • global_frame: [optional] frame id which is only used if the path was generated using a input file
  • path_file: [optional] yaml input file with three vectors named x, y and o. x and y are desribing the position and o the relative chassis orientation to the path
  • waypoints_distance: [default: 0.5 ] discretization distance to generate points on the path for spline generation
  • path_tmp_file: [default: /tmp/waypoint_to_spline.yaml ] if given a tmp file will be gernerated to store the points used to create the spline

arguments examples

_global_frame:=map _waypoints_distance:=0.5 path:=/global_planner/planner/plan __ns:=blue