RRT is a searching algorithm applied by building a space filling tree from samples drawn randomly of high-dimensional search spaces .
import networkx as nx import numpy as np from sklearn.neighbors import KDTree class RRT: def __init__(self,x_init): self.tree = nx.DiGraph() self.tree.add_node(x_init) def nearest_neighbour(x_rand,rrr): dist=1000 x_rand = np.array(x_rand) for node in rrt.tree.nodes: d = np.linalg.norm(x_rand - np.array(node)) if d < dist: dist = d vertex = node return vertex def rand_conf(): return np.random.uniform(0,100),np.random.uniform(0,100) def build_rrt(init_configuration, num_vertices_k, delta_distance): rrt = RRT(init_configuration) for k in num_vertices_k: x_rand = rand_conf() x_near = nearest__neighbour(x_rand,rrt) x_new = new_conf(x_near,x_rand,delta_distance) rtt.tree.add_node(x_new) rrt.tree.add_edge(tuple(x_near),tuple(x_new)) return rrt rrt = build_rrt((10,10),100,1)
Comments are closed, but trackbacks and pingbacks are open.