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279 lines
7.8 KiB
Python
279 lines
7.8 KiB
Python
"""
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Path Planning Sample Code with RRT*
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author: AtsushiSakai(@Atsushi_twi)
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"""
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import random
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import math
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import copy
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import numpy as np
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import matplotlib.pyplot as plt
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show_animation = True
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class RRT():
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"""
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Class for RRT Planning
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"""
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def __init__(self, start, goal, obstacleList, randArea,
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expandDis=0.5, goalSampleRate=20, maxIter=500):
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"""
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Setting Parameter
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start:Start Position [x,y]
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goal:Goal Position [x,y]
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obstacleList:obstacle Positions [[x,y,size],...]
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randArea:Ramdom Samping Area [min,max]
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"""
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self.start = Node(start[0], start[1])
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self.end = Node(goal[0], goal[1])
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self.minrand = randArea[0]
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self.maxrand = randArea[1]
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self.expandDis = expandDis
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self.goalSampleRate = goalSampleRate
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self.maxIter = maxIter
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self.obstacleList = obstacleList
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def Planning(self, animation=True):
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"""
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Pathplanning
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animation: flag for animation on or off
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"""
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self.nodeList = [self.start]
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for i in range(self.maxIter):
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rnd = self.get_random_point()
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nind = self.GetNearestListIndex(self.nodeList, rnd)
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newNode = self.steer(rnd, nind)
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# print(newNode.cost)
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if self.__CollisionCheck(newNode, self.obstacleList):
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nearinds = self.find_near_nodes(newNode)
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newNode = self.choose_parent(newNode, nearinds)
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self.nodeList.append(newNode)
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self.rewire(newNode, nearinds)
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if animation and i % 5 == 0:
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self.DrawGraph(rnd)
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# generate coruse
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lastIndex = self.get_best_last_index()
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if lastIndex is None:
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return None
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path = self.gen_final_course(lastIndex)
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return path
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def choose_parent(self, newNode, nearinds):
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if not nearinds:
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return newNode
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dlist = []
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for i in nearinds:
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dx = newNode.x - self.nodeList[i].x
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dy = newNode.y - self.nodeList[i].y
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d = math.sqrt(dx ** 2 + dy ** 2)
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theta = math.atan2(dy, dx)
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if self.check_collision_extend(self.nodeList[i], theta, d):
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dlist.append(self.nodeList[i].cost + d)
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else:
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dlist.append(float("inf"))
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mincost = min(dlist)
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minind = nearinds[dlist.index(mincost)]
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if mincost == float("inf"):
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print("mincost is inf")
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return newNode
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newNode.cost = mincost
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newNode.parent = minind
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return newNode
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def steer(self, rnd, nind):
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# expand tree
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nearestNode = self.nodeList[nind]
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theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
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newNode = Node(rnd[0], rnd[1])
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currentDistance = math.sqrt(
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(rnd[1] - nearestNode.y) ** 2 + (rnd[0] - nearestNode.x) ** 2)
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# Find a point within expandDis of nind, and closest to rnd
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if currentDistance <= self.expandDis:
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pass
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else:
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newNode.x = nearestNode.x + self.expandDis * math.cos(theta)
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newNode.y = nearestNode.y + self.expandDis * math.sin(theta)
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newNode.cost = float("inf")
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newNode.parent = None
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return newNode
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def get_random_point(self):
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if random.randint(0, 100) > self.goalSampleRate:
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rnd = [random.uniform(self.minrand, self.maxrand),
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random.uniform(self.minrand, self.maxrand)]
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else: # goal point sampling
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rnd = [self.end.x, self.end.y]
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return rnd
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def get_best_last_index(self):
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disglist = [self.calc_dist_to_goal(
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node.x, node.y) for node in self.nodeList]
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goalinds = [disglist.index(i) for i in disglist if i <= self.expandDis]
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if not goalinds:
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return None
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mincost = min([self.nodeList[i].cost for i in goalinds])
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for i in goalinds:
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if self.nodeList[i].cost == mincost:
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return i
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return None
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def gen_final_course(self, goalind):
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path = [[self.end.x, self.end.y]]
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while self.nodeList[goalind].parent is not None:
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node = self.nodeList[goalind]
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path.append([node.x, node.y])
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goalind = node.parent
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path.append([self.start.x, self.start.y])
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return path
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def calc_dist_to_goal(self, x, y):
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return np.linalg.norm([x - self.end.x, y - self.end.y])
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def find_near_nodes(self, newNode):
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nnode = len(self.nodeList)
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r = 50.0 * math.sqrt((math.log(nnode) / nnode))
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# r = self.expandDis * 5.0
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dlist = [(node.x - newNode.x) ** 2 +
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(node.y - newNode.y) ** 2 for node in self.nodeList]
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nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
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return nearinds
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def rewire(self, newNode, nearinds):
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nnode = len(self.nodeList)
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for i in nearinds:
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nearNode = self.nodeList[i]
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dx = newNode.x - nearNode.x
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dy = newNode.y - nearNode.y
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d = math.sqrt(dx ** 2 + dy ** 2)
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scost = newNode.cost + d
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if nearNode.cost > scost:
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theta = math.atan2(dy, dx)
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if self.check_collision_extend(nearNode, theta, d):
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nearNode.parent = nnode - 1
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nearNode.cost = scost
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def check_collision_extend(self, nearNode, theta, d):
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tmpNode = copy.deepcopy(nearNode)
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for i in range(int(d / self.expandDis)):
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tmpNode.x += self.expandDis * math.cos(theta)
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tmpNode.y += self.expandDis * math.sin(theta)
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if not self.__CollisionCheck(tmpNode, self.obstacleList):
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return False
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return True
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def DrawGraph(self, rnd=None):
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"""
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Draw Graph
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"""
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plt.clf()
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if rnd is not None:
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plt.plot(rnd[0], rnd[1], "^k")
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for node in self.nodeList:
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if node.parent is not None:
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plt.plot([node.x, self.nodeList[node.parent].x], [
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node.y, self.nodeList[node.parent].y], "-g")
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for (ox, oy, size) in self.obstacleList:
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plt.plot(ox, oy, "ok", ms=30 * size)
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plt.plot(self.start.x, self.start.y, "xr")
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plt.plot(self.end.x, self.end.y, "xr")
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plt.axis([-2, 15, -2, 15])
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plt.grid(True)
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plt.pause(0.01)
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def GetNearestListIndex(self, nodeList, rnd):
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dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
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** 2 for node in nodeList]
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minind = dlist.index(min(dlist))
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return minind
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def __CollisionCheck(self, node, obstacleList):
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for (ox, oy, size) in obstacleList:
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dx = ox - node.x
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dy = oy - node.y
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d = dx * dx + dy * dy
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if d <= size ** 2:
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return False # collision
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return True # safe
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class Node():
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"""
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RRT Node
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"""
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def __init__(self, x, y):
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self.x = x
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self.y = y
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self.cost = 0.0
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self.parent = None
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def main():
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print("Start rrt planning")
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# ====Search Path with RRT====
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obstacleList = [
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(5, 5, 1),
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(3, 6, 2),
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(3, 8, 2),
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(3, 10, 2),
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(7, 5, 2),
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(9, 5, 2)
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] # [x,y,size(radius)]
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# Set Initial parameters
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rrt = RRT(start=[0, 0], goal=[10, 10],
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randArea=[-2, 15], obstacleList=obstacleList)
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path = rrt.Planning(animation=show_animation)
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if path is None:
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print("Cannot find path")
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else:
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print("found path!!")
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# Draw final path
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if show_animation:
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rrt.DrawGraph()
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plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
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plt.grid(True)
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plt.pause(0.01) # Need for Mac
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plt.show()
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if __name__ == '__main__':
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main()
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