从0开始实现RRT路径规划算法（快速扩展随机树原理+python伪代码）

import numpy as np
 
class RRT:
    def __init__(self, x_bounds, y_bounds, obstacle_list):
        self.x_bounds = x_bounds
        self.y_bounds = y_bounds
        self.obstacle_list = obstacle_list
        # 其他初始化参数
 
    def nearest_neighbor(self, tree, point):
        distances = [self.euclidian_dist(node.position, point) for node in tree]
        min_dist_node = tree[distances.index(min(distances))]
        return min_dist_node
 
    def steer(self, from_node, to_point):
        # 这里应该实现向量对齐的函数
        pass
 
    def add_node(self, tree, new_node):
        tree.append(new_node)
 
    def extend_tree(self, start_node, target_point, tree, obstacle_list):
        nearest_node = self.nearest_neighbor(tree, target_point)
        steer_point = self.steer(nearest_node, target_point)
        if self.is_collision_free(nearest_node, steer_point, obstacle_list):
            new_node = Node(steer_point, nearest_node)
            self.add_node(tree, new_node)
            return new_node
        return None
 
    def is_collision_free(self, from_node, to_point, obstacle_list):
        # 这里应该实现无碰撞检查的函数
        pass
 
    def path_from_start(self, goal_node):
        path = []
        current_node = goal_node
        while current_node is not None:
            path.append(current_node.position)
            current_node = current_node.parent
        return path
 
    def plan(self, start_point, target_point):
        # 这里应该实现RRT路径规划的主循环
        pass
 
# 以下是辅助函数和Node类定义
class Node:
    def __init__(self, position, parent):
        self.position = np.array(position)
        self.parent = parent
 
    def __eq__(self, other):
        return np.array_equal(self.position, other.position)
 
# 其他辅助函数

这个伪代码实例提供了RRT算法的基本框架。在实际应用中，你需要实现具体的函数，如nearest_neighbor来找到最接近目标点的节点，steer来生成新的节点，is_collision_free来检查新节点是否与障碍物不碰撞，以及主循环函数plan来执行整个路径规划过程。这些函数的具体实现会依赖于你的应用环境和需求。