RRT

python_motion_planning.global_planner.sample_search.rrt.RRT

Bases: SampleSearcher

Class for 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	10000
goal_sample_rate	float	heuristic sample	0.05

Examples:

Python Console Session

>>> import python_motion_planning as pmp
>>> planner = pmp.RRT((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] Rapidly-Exploring Random Trees: A New Tool for Path Planning

extractPath(closed_list)

Extract the path based on the CLOSED list.

Parameters:

Name	Type	Description	Default
closed_list	dict	CLOSED list	required

Returns cost (float): the cost of planning path path (list): the planning path

generateRandomNode()

Generate a random node to extend exploring tree.

Returns:

Name	Type	Description
node	Node	a random node based on sampling

getNearest(node_list, node)

Get the node from node_list that is nearest to node.

Parameters:

Name	Type	Description	Default
node_list	list	exploring list	required
node	Node	currently generated node	required

Returns:

Name	Type	Description
node	Node	nearest node

plan()

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.