BiRRT
- class discopygal.solvers.rrt.birrt.BiRRT(n_join, nearest_neighbors_start_class=None, nearest_neighbors_end_class=None, **kwargs)
Bases:
RRT
Implementation of the BiRRT algorithm. We grow two trees - one rooted at start, the other at the end. Every once in a while (n_join) try to extend both trees toward the same sample. Supports multi-robot motion planning, though might be inefficient for more than two-three robots.
- Parameters:
num_landmarks (
int
) – number of landmarks to sampleeta (
FT
) – maximum distance when steeringn_join (
int
) – period of iterations when trying to grow the trees towards the same samplenearest_neighbors_start (
NearestNeighbors
orNone
) – a nearest neighbors algorithm for the tree grown from start. if None then use sklearn implementationnearest_neighbors_end (
NearestNeighbors
orNone
) – a nearest neighbors algorithm for the tree grown from end. if None then use sklearn implementationmetric (
Metric
orNone
) – a metric for weighing edges, can be different then the nearest_neighbors metric! If None then use euclidean metricsampler (
Sampler
) – sampling algorithm/method. if None then use uniform sampling
- build_roadmap()
Constructs the roadmap of points in the configuration space which a path will be searched on to find a solution. Every sampling solver should implement how to build the roadmap.
- Returns:
The built roadmap. Each node represents a point in configuration space (dimension = 2*robots_num)
- Return type:
- classmethod get_arguments()
Return a list of arguments and their description, defaults and types. Can be used by a GUI to generate fields dynamically. Should be overridded by solvers.
- Returns:
arguments dict
- Return type:
dict