ACO

python_motion_planning.global_planner.evolutionary_search.aco.ACO

Bases: EvolutionarySearcher

Class for Ant Colony Optimization(ACO) 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_ants	int	number of ants	50
alpha	float	pheromone and heuristic factor weight coefficient	1.0
beta	float	pheromone and heuristic factor weight coefficient	5.0
rho	float	evaporation coefficient	0.1
Q	float	pheromone gain	1.0
max_iter	int	maximum iterations	100

Examples:

Python Console Session

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

References

[1] Ant Colony Optimization: A New Meta-Heuristic

getNeighbor(node)

Find neighbors of node.

Parameters:

Name	Type	Description	Default
node	Node	current node	required

Returns:

Name	Type	Description
neighbors	list	neighbors of current node

plan()

Ant Colony Optimization(ACO) motion plan function.

Returns:

Name	Type	Description
cost	float	path cost
path	list	planning path

run()

Running both plannig and animation.