Motion Planning

Implementations of path planning algorithms

Grid based planners

Here we compare the performance of Dijkstras and AStar algorithms in finding a path from a start (yellow tile) to the goal (red tile). By default, the planner aims to find a path from the top-left corner tile to the top-right corner tile of the grid. It is assumed that the edges of the grid have equal weights.

usage:

Dijkstras: python3 grid_planners/main -n 10 -a dijsktras
Astar: python3 grid_planners/main -n 10 -a astar

Free space planners

usage:

RRT: python3 free_space_planners/main.py -a rrt
RRT*: python3 free_space_planners/main.py -a rrtstar
Informed-RRT*: python3 free_space_planners/main.py -a irrtstar
Bi-Directional-RRT*: python3 free_space_planners/main.py -a birrt
Kinodynamic-RRT*: python3 free_space_planners/main.py -a kdrrtstar

Path from [15, 15] to [190, 190] found with distance 388.033 after expanding 7000 nodes rrt ran for 0.390s

Path from [15, 15] to [190, 190] found with distance 271.359 after expanding 7000 nodes rrtstar ran for 1.726s

Path from [15, 15] to [190, 190] found with distance 256.380 after expanding 7000 nodes irrtstar ran for 4.364s

Path from [15, 15] to [190, 190] found with distance 312.847 after expanding 119 nodes in 0.00724s

Path from [15, 15] to [190, 190] found with distance 278.758 after expanding 186 nodes in 0.0227s

Path from [15, 15, 1.570] to [190, 190, 1.570] found with distance 277.634 after expanding 3000 nodes in 3.508s