汽车路径规划python

汽车路径规划主要是指找到从起点到终点的最短路径，同时要避免碰撞和遵守交通规则。以下是一个简单的基于Python的汽车路径规划算法示例：

首先，我们需要定义一个地图，可以使用二维数组表示。数组中的每个元素代表一个道路交叉口或路段，0表示可通过，1表示不可通过。

map = [
    [0, 0, 0, 0, 0],
    [0, 1, 1, 0, 0],
    [0, 0, 0, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 0, 0, 0, 0]
]

接下来，我们需要定义起点和终点坐标。

start = (0, 0)
end = (4, 4)

然后，我们可以使用A*算法来搜索最短路径。A*算法是一种启发式搜索算法，它可以快速找到从起点到终点的最短路径。

from queue import PriorityQueue

def heuristic(a, b):
    return abs(b[0] - a[0]) + abs(b[1] - a[1])

def astar_search(start, end, map):
    frontier = PriorityQueue()
    frontier.put(start, 0)
    came_from = {}
    cost_so_far = {}
    came_from[start] = None
    cost_so_far[start] = 0

    while not frontier.empty():
        current = frontier.get()

        if current == end:
            break

        for next in get_neighbors(current, map):
            new_cost = cost_so_far[current] + 1
            if next not in cost_so_far or new_cost < cost_so_far[next]:
                cost_so_far[next] = new_cost
                priority = new_cost + heuristic(end, next)
                frontier.put(next, priority)
                came_from[next] = current

    return came_from, cost_so_far

def get_neighbors(current, map):
    neighbors = []
    for i in range(-1, 2):
        for j in range(-1, 2):
            if i == 0 and j == 0:
                continue
            x = current[0] + i
            y = current[1] + j
            if x >= 0 and x < len(map) and y >= 0 and y < len(map[0]) and map[x][y] == 0:
                neighbors.append((x, y))
    return neighbors

最后，我们可以使用得到的路径来控制汽车移动。

def follow_path(path):
    for i in range(len(path) - 1):
        current = path[i]
        next = path[i+1]
        dx = next[0] - current[0]
        dy = next[1] - current[1]
        print("move %d,%d" % (dx, dy))

完整代码如下：

from queue import PriorityQueue

def heuristic(a, b):
    return abs(b[0] - a[0]) + abs(b[1] - a[1])

def astar_search(start, end, map):
    frontier = PriorityQueue()
    frontier.put(start, 0)
    came_from = {}
    cost_so_far = {}
    came_from[start] = None
    cost_so_far[start] = 0

    while not frontier.empty():
        current = frontier.get()

        if current == end:
            break

        for next in get_neighbors(current, map):
            new_cost = cost_so_far[current] + 1
            if next not in cost_so_far or new_cost < cost_so_far[next]:
                cost_so_far[next] = new_cost
                priority = new_cost + heuristic(end, next)
                frontier.put(next, priority)
                came_from[next] = current

    return came_from, cost_so_far

def get_neighbors(current, map):
    neighbors = []
    for i in range(-1, 2):
        for j in range(-1, 2):
            if i == 0 and j == 0:
                continue
            x = current[0] + i
            y = current[1] + j
            if x >= 0 and x < len(map) and y >= 0 and y < len(map[0]) and map[x][y] == 0:
                neighbors.append((x, y))
    return neighbors

def follow_path(path):
    for i in range(len(path) - 1):
        current = path[i]
        next = path[i+1]
        dx = next[0] - current[0]
        dy = next[1] - current[1]
        print("move %d,%d" % (dx, dy))

map = [
    [0, 0, 0, 0, 0],
    [0, 1, 1, 0, 0],
    [0, 0, 0, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 0, 0, 0, 0]
]

start = (0, 0)
end = (4, 4)

came_from, cost_so_far = astar_search(start, end, map)
path = [end]
while path[-1] != start:
    path.append(came_from[path[-1]])
path.reverse()

follow_path(path)

这是一个简单的例子，实际应用中需要考虑更多因素，例如车速、车道、交通信号等。但是这个例子可以作为一个基本的框架来进行扩展和修改。