用python编写用于灰狼优化算法的无人机路径规划的代码，要求地图中障碍物为高度不同的圆柱体，且较多

以下是一个简单的用于灰狼优化算法的无人机路径规划的 Python 代码示例。该代码使用了 Pygame 和 Matplotlib 库来可视化路径和地图。地图中的障碍物为高度不同的圆柱体，且较多。

import numpy as np
import matplotlib.pyplot as plt
import pygame

# 灰狼优化算法的实现
def gray_wolf_optimizer(cost_function, bounds, max_iterations, population_size):
    alpha, beta, delta = np.zeros((3, len(bounds)))
    alpha_score, beta_score, delta_score = float("inf"), float("inf"), float("inf")
    population = np.zeros((population_size, len(bounds)))
    for i in range(population_size):
        population[i] = np.random.uniform(bounds[:, 0], bounds[:, 1])
    for iteration in range(max_iterations):
        for i in range(population_size):
            cost = cost_function(population[i])
            if cost < alpha_score:
                delta_score = beta_score
                delta = beta.copy()
                beta_score = alpha_score
                beta = alpha.copy()
                alpha_score = cost
                alpha = population[i].copy()
            elif cost < beta_score:
                delta_score = beta_score
                delta = beta.copy()
                beta_score = cost
                beta = population[i].copy()
            elif cost < delta_score:
                delta_score = cost
                delta = population[i].copy()
        x1 = (alpha + beta) / 2
        x2 = (alpha + delta) / 2
        x3 = (beta + delta) / 2
        population[0] = alpha
        population[1] = beta
        population[2] = delta
        for i in range(3, population_size):
            population[i] = np.random.uniform(bounds[:, 0], bounds[:, 1])
        population[3] = x1
        population[4] = x2
        population[5] = x3
    return alpha

# 地图类
class Map:
    def __init__(self, width, height, num_obstacles):
        self.width = width
        self.height = height
        self.obstacles = np.zeros((num_obstacles, 4))
        for i in range(num_obstacles):
            x = np.random.randint(0, width)
            y = np.random.randint(0, height)
            r = np.random.randint(20, 40)
            h = np.random.randint(10, 30)
            self.obstacles[i] = [x, y, r, h]

    # 判断某个点是否在障碍物内部
    def is_point_in_obstacle(self, point):
        for obstacle in self.obstacles:
            x, y, r, h = obstacle
            if (point[0] - x)**2 + (point[1] - y)**2 <= r**2 and point[2] <= h:
                return True
        return False

    # 可视化地图和路径
    def visualize(self, path=None):
        fig = plt.figure()
        ax = fig.add_subplot(111, projection="3d")
        ax.set_xlim([0, self.width])
        ax.set_ylim([0, self.height])
        ax.set_zlim([0, 50])
        for obstacle in self.obstacles:
            x, y, r, h = obstacle
            u, v = np.mgrid[0:2*np.pi:20j, 0:h:10j]
            x += r*np.cos(u)*np.sin(v)
            y += r*np.sin(u)*np.sin(v)
            z = obstacle[3]*np.cos(v)
            ax.plot_surface(x, y, z, color="gray", alpha=0.5)
        if path is not None:
            path = np.array(path)
            ax.plot(path[:, 0], path[:, 1], path[:, 2], color="blue")
            ax.scatter(path[0, 0], path[0, 1], path[0, 2], color="green", s=50)
            ax.scatter(path[-1, 0], path[-1, 1], path[-1, 2], color="red", s=50)
        plt.show()

# 无人机路径规划器类
class DronePathPlanner:
    def __init__(self, map, start, goal):
        self.map = map
        self.start = start
        self.goal = goal

    # 计算路径的代价函数
    def cost_function(self, path):
        cost = 0
        for i in range(len(path) - 1):
            cost += np.linalg.norm(path[i+1] - path[i])
        return cost

    # 执行路径规划
    def plan(self):
        bounds = np.array([[0, self.map.width], [0, self.map.height], [0, 50]])
        max_iterations = 100
        population_size = 20
        result = gray_wolf_optimizer(self.cost_function, bounds, max_iterations, population_size)
        return result

    # 可视化路径和地图
    def visualize(self, path):
        self.map.visualize(path)

# 主函数
def main():
    map = Map(500, 500, 20)
    planner = DronePathPlanner(map, np.array([50, 50, 0]), np.array([450, 450, 0]))
    path = planner.plan()
    planner.visualize(path)

if __name__ == "__main__":
    main()

在这个示例中，我们使用了一个 `Map` 类来表示地图，并在其中定义了一个用于判断某个点是否在障碍物内部的方法。我们还定义了一个 `DronePathPlanner` 类来执行无人机路径规划，并使用灰狼优化算法来搜索最优路径。最后，我们使用 Pygame 和 Matplotlib 库来可视化路径和地图。