用python写一个基于人工势场法的机器人路径规划的代码

```python import numpy as np class Robot: def __init__(self, start, goal, obstacles, world_size): self.start = start self.goal = goal self.obstacles = obstacles self.world_size = world_size self.obstacle_potential_gain = 100 self.goal_potential_gain = 100 self.step_size = 1 self.max_iterations = 1000 def calculate_potential_field(self, position): total_potential = 0 for obstacle in self.obstacles: distance = np.linalg.norm(np.array(position) - np.array(obstacle)) total_potential += 0.5 * self.obstacle_potential_gain * distance2 total_potential += 0.5 * self.goal_potential_gain * np.linalg.norm(np.array(position) - np.array(self.goal))2 return total_potential def plan_path(self): current_position = self.start path = [current_position] for _ in range(self.max_iterations): gradient = np.array([0, 0]) for x in np.arange(-1, 2, 1): for y in np.arange(-1, 2, 1): if x==0 and y==0: continue next_position = current_position + self.step_size * np.array([x, y]) if not (0 <= next_position[0] < self.world_size[0] and 0 <= next_position[1] < self.world_size[1]): continue if next_position in self.obstacles: continue next_potential = self.calculate_potential_field(next_position) current_potential = self.calculate_potential_field(current_position) if next_potential < current_potential: gradient += (current_position - next_position) next_position = current_position + self.step_size * gradient if np.array_equal(next_position, current_position): break path.append(next_position) current_position = next_position if np.linalg.norm(next_position - self.goal) < self.step_size: break return path # Define start, goal, obstacles and world size start = [0, 0] goal = [10, 10] obstacles = [[2, 2], [3, 3], [4, 4], [5, 5]] world_size = [15, 15] # Plan path for the robot robot = Robot(start, goal, obstacles, world_size) path = robot.plan_path() print(path) ``` 这段代码实现了基于人工势场法的机器人路径规划。首先定义了机器人类Robot，包括起点、终点、障碍物、世界大小等属性。然后定义了计算势场的方法calculate_potential_field以及规划路径的方法plan_path。在规划路径的过程中，利用梯度下降法对机器人进行移动，直到达到终点或者达到最大迭代次数为止。打印出规划好的路径。 [2024-11-09 15:45:23 | AI写代码神器 | 1828点数解答]