PRM

class discopygal.solvers.prm.prm.PRM(num_landmarks, k, **kwargs)

Bases: SamplingSolver

The basic implementation of a Probabilistic Road Map (PRM) solver. Supports multi-robot motion planning, though might be inefficient for more than two-three robots.

Parameters:

  • num_landmarks (int) – number of landmarks to sample

  • k (int) – number of nearest neighbors to connect

  • nearest_neighbors (NearestNeighbors or None) – a nearest neighbors algorithm. if None then use sklearn implementation

  • metric (Metric or None) – a metric for weighing edges, can be different then the nearest_neighbors metric! If None then use euclidean metric

  • sampler (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:

nx.Graph

static 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