自动驾驶路径规划算法（RRT）–基础知识篇

大家好，欢迎来到IT知识分享网。

RRT是一种多维空间中有效率的规划方法。它以一个初始点作为根节点，通过随机采样增加叶子节点的方式，生成一个随机扩展树，当随机树中的叶子节点包含了目标点或进入了目标区域，便可以在随机树中找到一条由从初始点到目标点的路径，是一种单查询算法目标是尽可能快的找到一条从起点到终点的可行路径

1. 算法通常将起点作为根节点$x_o$加入到随机树的节点集合中
2. 在可行区域内随机选取一个节点$x_r$，并在已生成的树中利用欧氏距离判断距离$x_r$最近的点$x_n$，
3. 从$x_r$到$x_n$的方向上扩展固定步长为u的线段（如果两个节点之间的距离小于u，直接把节点$x_r$当作新的节点），得到新的节点$x_N$,如果新的节点和$x_N$之间没有障碍物就将这个新节点加入到树中，并对节点的父节点进行设置。
4. 若两个节点之间有障碍物，就重新选择节点进行扩展
5. 循环执行以上步骤，直到随机树的叶节点包含了目标点，并从中找出一条各节点连接成的从起点至终点的无碰撞路径
   伪码如图所示
   
   
   

 import math import random import matplotlib.pyplot as plt import numpy as np from celluloid import Camera # 保存动图时用，pip install celluloid class RRT: """ Class for RRT planning """ class Node: """ 创建节点 """ def __init__(self, x, y): self.x = x # 节点坐标 self.y = y self.path_x = [] # 路径，作为画图的数据 self.path_y = [] self.parent = None #父节点 class AreaBounds: """区域大小 """ def __init__(self, area): self.xmin = float(area[0]) self.xmax = float(area[1]) self.ymin = float(area[2]) self.ymax = float(area[3]) def __init__(self, start, goal, obstacle_list, rand_area, expand_dis=3.0, goal_sample_rate=5, max_iter=500, play_area=None, robot_radius=0.0, ): """ Setting Parameter start:起点 [x,y] goal:目标点 [x,y] obstacleList:障碍物位置列表 [[x,y,size],...] rand_area: 采样区域 x,y ∈ [min,max] play_area: 约束随机树的范围 [xmin,xmax,ymin,ymax] robot_radius: 机器人半径 expand_dis: 扩展的步长 goal_sample_rate: 采样目标点的概率，百分制.default: 5，即表示5%的概率直接采样目标点 """ self.start = self.Node(start[0], start[1]) # 根节点 self.end = self.Node(goal[0], goal[1]) self.min_rand = rand_area[0] self.max_rand = rand_area[1] if play_area is not None: self.play_area = self.AreaBounds(play_area) else: self.play_area = None self.expand_dis = expand_dis self.goal_sample_rate = goal_sample_rate self.max_iter = max_iter self.obstacle_list = obstacle_list self.node_list = [] self.robot_radius = robot_radius def planning(self, animation=True,camara=None): """ rrt path planning animation: flag for animation on or off camara: 是否保存动图 """ # 将起点作为根节点x_{ 
   init}​，加入到随机树的节点集合中。 self.node_list = [self.start] for i in range(self.max_iter): # 从可行区域内随机选取一个节点x_{ 
   rand} rnd_node = self.sample_free() # 已生成的树中利用欧氏距离判断距离x_{ 
   rand}​最近的点x_{ 
   near}。 nearest_ind = self.get_nearest_node_index(self.node_list, rnd_node) nearest_node = self.node_list[nearest_ind] # 从x_{ 
   near}与x_{ 
   rand}的连线方向上扩展固定步长u，得到新节点 x_{ 
   new} new_node = self.steer(nearest_node, rnd_node, self.expand_dis) # 如果在可行区域内，且x_{ 
   near}与x_{ 
   new}之间无障碍物 if self.is_inside_play_area(new_node, self.play_area) and \ self.obstacle_free(new_node, self.obstacle_list, self.robot_radius): self.node_list.append(new_node) # 如果此时得到的节点x_new到目标点的距离小于扩展步长，则直接将目标点作为x_rand。 if self.calc_dist_to_goal(self.node_list[-1].x,self.node_list[-1].y) <= self.expand_dis: final_node = self.steer(self.node_list[-1], self.end,self.expand_dis) if self.obstacle_free(final_node, self.obstacle_list, self.robot_radius): # 返回最终路径 return self.generate_final_course(len(self.node_list) - 1) if animation and i % 5 ==0: self.draw_graph(rnd_node, camara) return None # cannot find path def steer(self, from_node, to_node, extend_length=float("inf")): """连线方向扩展固定步长查找x_new Args: from_node (_type_): x_near to_node (_type_): x_rand extend_length (_type_, optional): 扩展步长u. Defaults to float("inf"). Returns: _type_: _description_ """ # 利用反正切计算角度, 然后利用角度和步长计算新坐标 d, theta = self.calc_distance_and_angle(from_node, to_node) # 如果$x_{ 
   near}$与$x_{ 
   rand}$间的距离小于步长，则直接将$x_{ 
   rand}$作为新节点$x_{ 
   new}$ if extend_length >= d: new_x = to_node.x new_y = to_node.y else: new_x = from_node.x+math.cos(theta)*extend_length new_y = from_node.y+math.sin(theta)*extend_length new_node = self.Node(new_x,new_y) new_node.path_x = [from_node.x] new_node.path_y = [from_node.y] new_node.path_x.append(new_x) new_node.path_y.append(new_y) new_node.parent = from_node return new_node def generate_final_course(self, goal_ind): """生成路径 Args: goal_ind (_type_): 目标点索引 Returns: _type_: _description_ """ path = [[self.end.x, self.end.y]] node = self.node_list[goal_ind] while node.parent is not None: path.append([node.x, node.y]) node = node.parent path.append([node.x, node.y]) return path def calc_dist_to_goal(self, x, y): """计算(x,y)离目标点的距离 """ dx = x - self.end.x dy = y - self.end.y return math.hypot(dx, dy) def sample_free(self): # 以（100-goal_sample_rate）%的概率随机生长，(goal_sample_rate)%的概率朝向目标点生长 if random.randint(0, 100) > self.goal_sample_rate: rnd = self.Node( random.uniform(self.min_rand, self.max_rand), random.uniform(self.min_rand, self.max_rand)) else: # goal point sampling rnd = self.Node(self.end.x, self.end.y) return rnd def draw_graph(self, rnd=None, camera=None): if camera==None: plt.clf() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect( 'key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) # 画随机点 if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") if self.robot_radius > 0.0: self.plot_circle(rnd.x, rnd.y, self.robot_radius, '-r') # 画已生成的树 for node in self.node_list: if node.parent: plt.plot(node.path_x, node.path_y, "-g") # 画障碍物 for (ox, oy, size) in self.obstacle_list: self.plot_circle(ox, oy, size) # 如果约定了可行区域，则画出可行区域 if self.play_area is not None: plt.plot([self.play_area.xmin, self.play_area.xmax, self.play_area.xmax, self.play_area.xmin, self.play_area.xmin], [self.play_area.ymin, self.play_area.ymin, self.play_area.ymax, self.play_area.ymax, self.play_area.ymin], "-k") # 画出起点和目标点 plt.plot(self.start.x, self.start.y, "xr") plt.plot(self.end.x, self.end.y, "xr") plt.axis("equal") plt.axis([-2, 15, -2, 15]) plt.grid(True) plt.pause(0.01) if camera!=None: camera.snap() # 静态方法无需实例化，也可以实例化后调用，静态方法内部不能调用self.的变量 @staticmethod def plot_circle(x, y, size, color="-b"): # pragma: no cover deg = list(range(0, 360, 5)) deg.append(0) xl = [x + size * math.cos(np.deg2rad(d)) for d in deg] yl = [y + size * math.sin(np.deg2rad(d)) for d in deg] plt.plot(xl, yl, color) @staticmethod def get_nearest_node_index(node_list, rnd_node): dlist = [(node.x - rnd_node.x)**2 + (node.y - rnd_node.y)**2 for node in node_list] minind = dlist.index(min(dlist)) return minind @staticmethod def is_inside_play_area(node, play_area): if play_area is None: return True # no play_area was defined, every pos should be ok if node.x < play_area.xmin or node.x > play_area.xmax or \ node.y < play_area.ymin or node.y > play_area.ymax: return False # outside - bad else: return True # inside - ok @staticmethod def obstacle_free(node, obstacleList, robot_radius): if node is None: return False for (ox, oy, size) in obstacleList: dx_list = [ox - x for x in node.path_x] dy_list = [oy - y for y in node.path_y] d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)] if min(d_list) <= (size+robot_radius)**2: return False # collision return True # safe @staticmethod def calc_distance_and_angle(from_node, to_node): """计算两个节点间的距离和方位角 Args: from_node (_type_): _description_ to_node (_type_): _description_ Returns: _type_: _description_ """ dx = to_node.x - from_node.x dy = to_node.y - from_node.y d = math.hypot(dx, dy) theta = math.atan2(dy, dx) return d, theta def main(gx=6.0, gy=10.0): print("start " + __file__) fig = plt.figure(1) camera = Camera(fig) # 保存动图时使用 camera = None # 不保存动图时，camara为None show_animation = True # ====Search Path with RRT==== obstacleList = [(5, 5, 1), (3, 6, 2), (3, 8, 2), (3, 10, 2), (7, 5, 2), (9, 5, 2), (8, 10, 1)] # [x, y, radius] # Set Initial parameters rrt = RRT( start=[0, 0], goal=[gx, gy], rand_area=[-2, 15], obstacle_list=obstacleList, play_area=[-2, 12, 0, 14], robot_radius=0.8 ) path = rrt.planning(animation=show_animation,camara=camera) if path is None: print("Cannot find path") else: print("found path!!") # Draw final path if show_animation: rrt.draw_graph(camera=camera) plt.grid(True) plt.pause(0.01) plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r') if camera!=None: camera.snap() animation = camera.animate() animation.save('trajectory.gif') plt.show() if __name__ == '__main__': main()