InformedRRT

python_motion_planning.global_planner.sample_search.informed_rrt.InformedRRT

Bases: RRTStar

Class for Informed RRT* motion planning.

Parameters:

Name	Type	Description	Default
start	tuple	start point coordinate	required
goal	tuple	goal point coordinate	required
env	Env	environment	required
max_dist	float	Maximum expansion distance one step	0.5
sample_num	int	Maximum number of sample points	1500
r	float	optimization radius	12.0
goal_sample_rate	float	heuristic sample	0.05

Examples:

Python Console Session

>>> import python_motion_planning as pmp
>>> planner = pmp.InformedRRT((5, 5), (45, 25), pmp.Map(51, 31))
>>> cost, path, expand = planner.plan()     # planning results only
>>> planner.plot.animation(path, str(planner), cost, expand)  # animation
>>> planner.run()       # run both planning and animation

References

[1] Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal heuristic

generateRandomNode()

Generate a random node to extend exploring tree.

Returns:

Name	Type	Description
node	Node	a random node based on sampling

plan()

Informed-RRT* motion plan function.

Returns:

Name	Type	Description
cost	float	path cost
path	list	planning path
expand	list	expanded (sampled) nodes list

run()

Running both plannig and animation.