LPAStar

python_motion_planning.global_planner.graph_search.lpa_star.LPAStar

Bases: GraphSearcher

Class for LPA* 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	'euclidean'

Examples:

Python Console Session

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

References

[1] Lifelong Planning A*

OnPress(event)

Mouse button callback function.

Parameters:

Name	Type	Description	Default
event	MouseEvent	mouse event	required

calculateKey(node)

Calculate priority of node.

Parameters:

Name	Type	Description	Default
node	LNode	current node	required

Returns:

Name	Type	Description
key	list	priority of node

computeShortestPath()

Perceived dynamic obstacle information to optimize global path.

extractPath()

Extract the path based on greedy policy.

Return

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

getNeighbor(node)

Find neighbors of node.

Parameters:

Name	Type	Description	Default
node	LNode	current node	required

Returns:

Name	Type	Description
neighbors	list	neighbors of node

plan()

LPA* dynamic motion planning function.

Returns:

Name	Type	Description
cost	float	path cost
path	list	planning path
_	None	None

run()

Running both plannig and animation.

updateVertex(node)

Update the status and the current cost to node and it's neighbor.

Parameters:

Name	Type	Description	Default
node	LNode	current node	required