运用栅格法，通过粒子群算法实现三维空间路径规划，请给我一段示范性代码

好的，下面是一个使用栅格法和粒子群算法（PSO）实现三维空间路径规划的示范性代码。这个代码示例展示了如何将三维空间划分为栅格，并使用粒子群算法来寻找最优路径。 ```python import numpy as np # 定义栅格地图 class GridMap: def __init__(self, size): self.size = size self.obstacles = set() def set_obstacle(self, x, y, z): self.obstacles.add((x, y, z)) def is_obstacle(self, x, y, z): return (x, y, z) in self.obstacles # 定义粒子 class Particle: def __init__(self, size): self.position = np.random.uniform(0, size, 3) self.velocity = np.random.uniform(-1, 1, 3) self.best_position = np.copy(self.position) def update_velocity(self, global_best_position, inertia, cognitive, social): r1, r2 = np.random.rand(2) cognitive_velocity = cognitive * r1 * (self.best_position - self.position) social_velocity = social * r2 * (global_best_position - self.position) self.velocity = inertia * self.velocity + cognitive_velocity + social_velocity def update_position(self, size): self.position += self.velocity self.position = np.clip(self.position, 0, size) # 粒子群算法 def pso(grid_map, num_particles, num_iterations, inertia, cognitive, social): size = grid_map.size particles = [Particle(size) for _ in range(num_particles)] global_best_position = None global_best_value = float('inf') for _ in range(num_iterations): for particle in particles: if grid_map.is_obstacle(*particle.position): continue value = np.linalg.norm(particle.position - np.array([size, size, size])) if value < global_best_value: global_best_value = value global_best_position = particle.position if value < np.linalg.norm(particle.best_position - np.array([size, size, size])): particle.best_position = particle.position for particle in particles: particle.update_velocity(global_best_position, inertia, cognitive, social) particle.update_position(size) return global_best_position # 示例使用 grid_map = GridMap(10) grid_map.set_obstacle(2, 3, 4) grid_map.set_obstacle(5, 6, 7) grid_map.set_obstacle(8, 2, 3) best_path = pso(grid_map, num_particles=30, num_iterations=100, inertia=0.5, cognitive=1.5, social=1.5) print("最优路径:", best_path) ``` 这个代码示例中，我们定义了一个`GridMap`类来表示三维栅格地图，并使用`Particle`类来表示粒子。`pso`函数实现了粒子群算法，通过迭代更新粒子的速度和位置来寻找最优路径。