人工场势法路径规划三维代码

以下是一个简单的三维路径规划代码，基于人工势场法实现：

import numpy as np

class Point:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z

class Obstacle:
    def __init__(self, center, radius):
        self.center = center
        self.radius = radius

class PathPlanning:
    def __init__(self, start, goal, obstacles, eta=0.5, rho=0.5, max_iter=1000, epsilon=0.1):
        self.start = start
        self.goal = goal
        self.obstacles = obstacles
        self.eta = eta
        self.rho = rho
        self.max_iter = max_iter
        self.epsilon = epsilon

    def distance(self, p1, p2):
        return np.sqrt((p1.x - p2.x) ** 2 + (p1.y - p2.y) ** 2 + (p1.z - p2.z) ** 2)

    def attractive_force(self, q, q_goal):
        return self.eta * (q_goal - q) / self.distance(q, q_goal)

    def repulsive_force(self, q, obstacles):
        f = np.zeros(3)
        for obstacle in obstacles:
            d = self.distance(q, obstacle.center)
            if d < obstacle.radius:
                f += self.rho * ((1 / d) - (1 / obstacle.radius)) * ((q - obstacle.center) / d ** 3)
        return f

    def plan(self):
        q = Point(self.start[0], self.start[1], self.start[2])
        q_goal = Point(self.goal[0], self.goal[1], self.goal[2])
        i = 0
        while i < self.max_iter and self.distance(q, q_goal) > self.epsilon:
            f_att = self.attractive_force(q, q_goal)
            f_rep = self.repulsive_force(q, self.obstacles)
            f_total = f_att + f_rep
            q.x += f_total[0]
            q.y += f_total[1]
            q.z += f_total[2]
            i += 1
        if i == self.max_iter:
            return None
        else:
            return [q.x, q.y, q.z]

这个代码实现的是一个简单的三维路径规划算法，其中包括起点、终点和障碍物的定义和计算距离的方法。在 `plan()` 方法中，我们使用人工势场法来计算每个点的位移，以使机器人能够避开障碍物并朝着目标点移动。具体来说，我们计算了两种力：吸引力和斥力。吸引力是指机器人被目标点吸引的力，而斥力是指机器人被障碍物排斥的力。在每个迭代中，我们计算总力，并将其用于更新机器人位置，直到机器人到达目标点或达到最大迭代次数。

使用示例：

start = [0, 0, 0]
goal = [10, 10, 10]
obstacles = [Obstacle(Point(5, 5, 5), 2)]
planner = PathPlanning(start, goal, obstacles)
path = planner.plan()
print(path)

这个示例定义了起点、终点和一个球形障碍物，然后使用 `PathPlanning` 类计算路径。在这种情况下，机器人应该绕过障碍物并沿着直线路径到达目标点。