基于Python的飞机模型路径规划

基于Python的飞机模型路径规划可以使用多种算法，其中最常见的是A*算法和Dijkstra算法。以下是一个简单的基于A*算法的示例代码，可以实现寻找无人机的最短路径。

import math
import heapq

# 定义节点类
class Node:
    def __init__(self, x, y, z):
        self.x = x
        self.y = y
        self.z = z
        self.g = 0
        self.h = 0
        self.f = 0
        self.parent = None

    # 定义估价函数
    def heuristic(self, goal):
        dx = abs(self.x - goal.x)
        dy = abs(self.y - goal.y)
        dz = abs(self.z - goal.z)
        return math.sqrt(dx*dx + dy*dy + dz*dz)

    # 定义比较运算符
    def __lt__(self, other):
        return self.f < other.f

# 定义A*算法函数
def astar(start, goal, obstacles):
    open_set = []
    closed_set = set()

    heapq.heappush(open_set, start)
    
    while len(open_set) > 0:
        current = heapq.heappop(open_set)

        if current == goal:
            path = []
            while current is not None:
                path.append(current)
                current = current.parent
            return path[::-1]

        closed_set.add(current)

        for neighbor in get_neighbors(current, obstacles):
            if neighbor in closed_set:
                continue

            tentative_g = current.g + current.heuristic(neighbor)
            if neighbor not in open_set or tentative_g < neighbor.g:
                neighbor.g = tentative_g
                neighbor.h = neighbor.heuristic(goal)
                neighbor.f = neighbor.g + neighbor.h
                neighbor.parent = current

                if neighbor not in open_set:
                    heapq.heappush(open_set, neighbor)

def get_neighbors(node, obstacles):
    neighbors = []

    for dx in range(-1, 2):
        for dy in range(-1, 2):
            for dz in range(-1, 2):
                if dx == 0 and dy == 0 and dz == 0:
                    continue

                x = node.x + dx
                y = node.y + dy
                z = node.z + dz

                if (x, y, z) in obstacles:
                    continue

                neighbor = Node(x, y, z)
                neighbors.append(neighbor)

    return neighbors

# 示例用法
start = Node(0, 0, 0)
goal = Node(10, 10, 10)
obstacles = {(1, 1, 1), (2, 2, 2), (3, 3, 3), (4, 4, 4)}
path = astar(start, goal, obstacles)

print("Path:")
for node in path:
    print(node.x, node.y, node.z)

在以上示例代码中，使用了一个名为Node的类来表示节点，其中包含节点的坐标（x、y、z）以及g、h和f值（g代表从起点到该节点的实际代价，h代表该节点到目标节点的估计代价，f=g+h）。使用堆来实现open set，确保按f值从小到大排序，从而选择f值最小的节点作为下一个扩展的节点。在get_neighbors函数中，生成每个节点周围的8个邻居节点，并排除掉障碍物。最后，通过调用astar函数，可以获取起点到终点的最短路径。