VoronoiPlanner

python_motion_planning.global_planner.graph_search.voronoi.VoronoiPlanner

Bases: GraphSearcher

Class for Voronoi-based motion planning.

Parameters:

Name	Type	Description	Default
start	tuple	start point coordinate	required
goal	tuple	goal point coordinate	required
env	Env	environment	required
heuristic_type	str	heuristic function type, default is euclidean	'euclidean'
n_knn	int	number of edges from one sampled point	10
max_edge_len	float	maximum edge length	10.0
inflation_r	float	inflation range	1.0

Examples:

Python Console Session

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

extractPath(closed_list)

Extract the path based on the CLOSED list.

Parameters:

Name	Type	Description	Default
closed_list	dict	CLOSED list	required

Returns:

Name	Type	Description
cost	float	the cost of planning path
path	list	the planning path

getShortestPath(road_map, dijkstra=True)

Calculate shortest path using graph search algorithm(A*, Dijkstra, etc).

Parameters:

Name	Type	Description	Default
road_map	dict	road map for voronoi diagram, which store KNN for one voronoi node	required
dijkstra	bool	using Dijkstra if true, else A*	True

Returns:

Name	Type	Description
cost	float	path cost
path	list	planning path

isCollision(node1, node2)

Judge collision when moving from node1 to node2.

Parameters:

Name	Type	Description	Default
node1	Node	start node	required
node2	Node	end node	required

Returns:

Name	Type	Description
collision	bool	True if collision exists else False

plan()

Voronoi-based motion plan function.

Returns:

Name	Type	Description
cost	float	path cost
path	list	planning path
expand	list	voronoi sampled nodes

run()

Running both plannig and animation.