python for dynamo路径规划代码

以下是一个使用Python编写的Dynamo路径规划代码的示例：

import numpy as np
import matplotlib.pyplot as plt

# 定义起点和终点
start = (0, 0)
goal = (10, 10)

# 定义障碍物
obstacles = [(5, 5), (6, 6), (7, 7), (8, 8)]

# 定义地图大小
map_size = 11

# 定义代价地图
cost_map = np.ones((map_size, map_size))
for obstacle in obstacles:
    cost_map[obstacle] = np.inf

# 定义动态规划函数
def dynamic_programming(start, goal, cost_map):
    value_map = np.zeros_like(cost_map)
    policy_map = np.zeros_like(cost_map, dtype=int)
    directions = np.array([[0, 1], [1, 0], [0, -1], [-1, 0]])

    def is_valid_state(state):
        x, y = state
        if x < 0 or x >= map_size or y < 0 or y >= map_size:
            return False
        if np.isinf(cost_map[state]):
            return False
        return True

    def get_valid_neighbors(state):
        neighbors = []
        for direction in directions:
            neighbor = tuple(np.array(state) + direction)
            if is_valid_state(neighbor):
                neighbors.append(neighbor)
        return neighbors

    def get_value(state):
        if state == goal:
            return 0
        if np.isinf(cost_map[state]):
            return np.inf
        neighbors = get_valid_neighbors(state)
        values = []
        for neighbor in neighbors:
            value = cost_map[neighbor] + value_map[neighbor]
            values.append(value)
        return np.min(values)

    def get_policy(state):
        if state == goal:
            return -1
        if np.isinf(cost_map[state]):
            return -1
        neighbors = get_valid_neighbors(state)
        values = []
        for neighbor in neighbors:
            value = cost_map[neighbor] + value_map[neighbor]
            values.append(value)
        return np.argmin(values)

    for i in range(map_size):
        for j in range(map_size):
            state = (i, j)
            value_map[state] = get_value(state)
            policy_map[state] = get_policy(state)

    return value_map, policy_map

# 运行动态规划函数
value_map, policy_map = dynamic_programming(start, goal, cost_map)

# 可视化结果
plt.imshow(value_map, cmap='jet')
plt.colorbar()
for obstacle in obstacles:
    plt.plot(obstacle[1], obstacle[0], 'sk')
plt.plot(start[1], start[0], 'bo')
plt.plot(goal[1], goal[0], 'ro')
plt.show()

该代码使用动态规划算法计算从起点到终点的最短路径，并考虑了障碍物的影响。在代码中，我们首先定义了起点、终点和障碍物的位置，然后定义了地图大小和代价地图。接着，我们实现了一个动态规划函数，该函数计算了每个状态的最小代价和最优策略。最后，我们使用Matplotlib库可视化了结果。