mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
yue qi
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
# Real-Time Randomized Path Planning for Robot Navigation∗
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"""
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This is rrt extend code for 3D
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@author: yue qi
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"""
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import numpy as np
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from numpy.matlib import repmat
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from collections import defaultdict
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import time
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import matplotlib.pyplot as plt
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Sampling_based_Planning/")
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from rrt_3D.env3D import env
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from rrt_3D.utils3D import getDist, sampleFree, nearest, steer, isCollide, near, visualization, cost, path
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# here attempt to use a KD tree for the data structure implementation
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import scipy.spatial.kdtree as KDtree
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class extend_rrt(object):
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def __init__(self):
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self.env = env()
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self.x0, self.xt = tuple(self.env.start), tuple(self.env.goal)
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self.current = tuple(self.env.start)
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self.stepsize = 0.5
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self.maxiter = 10000
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self.GoalProb = 0.05 # probability biased to the goal
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self.WayPointProb = 0.05 # probability falls back on to the way points
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self.done = False
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self.V = [] # vertices
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self.Parent = {}
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self.Path = []
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self.ind = 0
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self.i = 0
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#--------- basic rrt algorithm----------
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def RRTplan(self, env, initial, goal):
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threshold = self.stepsize
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nearest = initial # state structure
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self.V.append(initial)
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rrt_tree = initial # TODO KDtree structure
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while self.ind <= self.maxiter:
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target = self.ChooseTarget(goal)
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nearest = self.Nearest(rrt_tree, target)
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extended, collide = self.Extend(env, nearest, target)
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if not collide:
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self.AddNode(rrt_tree, nearest, extended)
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if getDist(nearest, goal) <= threshold:
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self.AddNode(rrt_tree, nearest, self.xt)
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break
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self.i += 1
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self.ind += 1
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visualization(self)
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# return rrt_tree
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self.done = True
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self.Path, D = path(self)
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visualization(self)
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plt.show()
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def Nearest(self, tree, target):
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# TODO use kdTree to speed up search
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return nearest(self, target, isset=True)
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def Extend(self, env, nearest, target):
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extended, dist = steer(self, nearest, target, DIST = True)
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collide, _ = isCollide(self, nearest, target, dist)
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return extended, collide
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def AddNode(self, tree, nearest, extended):
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# TODO use a kdtree
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self.V.append(extended)
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self.Parent[extended] = nearest
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def RandomState(self):
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# generate a random, obstacle free state
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xrand = sampleFree(self, bias=0)
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return xrand
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#--------- insight to the rrt_extend
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def ChooseTarget(self, state):
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# return the goal, or randomly choose a state in the waypoints based on probs
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p = np.random.uniform()
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if len(self.V) == 1:
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i = 0
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else:
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i = np.random.randint(0, high = len(self.V) - 1)
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if 0 < p < self.GoalProb:
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return self.xt
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elif self.GoalProb < p < self.GoalProb + self.WayPointProb:
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return self.V[i]
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elif self.GoalProb + self.WayPointProb < p < 1:
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return tuple(self.RandomState())
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if __name__ == '__main__':
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t = extend_rrt()
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_ = t.RRTplan(t.env, t.x0, t.xt) |