基于经纬度和高度以及大量约束条件下的无人机航路规划A星算法Python实现

以下是基于经纬度和高度以及大量约束条件下的无人机航路规划A星算法Python实现的示例代码：

import math

# 地球半径，单位：米
EARTH_RADIUS = 6371000

# 目标点经纬度坐标
GOAL_LATITUDE = 31.23
GOAL_LONGITUDE = 121.47

# 起点经纬度坐标
START_LATITUDE = 31.22
START_LONGITUDE = 121.47

# 航路规划中的障碍物点经纬度坐标
OBSTACLES = [(31.22, 121.48), (31.23, 121.46), (31.24, 121.47)]

# 无人机飞行高度，单位：米
FLIGHT_HEIGHT = 100

# 无人机飞行速度，单位：米/秒
FLIGHT_SPEED = 20

# 无人机飞行时每个航路点之间的最小距离，单位：米
MINIMUM_DISTANCE = 10


class Node:
    def __init__(self, latitude, longitude, height, parent=None):
        self.latitude = latitude
        self.longitude = longitude
        self.height = height
        self.parent = parent
        self.g = 0
        self.h = 0
        self.f = 0

    def __eq__(self, other):
        return self.latitude == other.latitude and self.longitude == other.longitude and self.height == other.height

    def __hash__(self):
        return hash((self.latitude, self.longitude, self.height))


def get_distance(node1, node2):
    """
    计算两个节点之间的距离
    :param node1: 节点1
    :param node2: 节点2
    :return: 距离，单位：米
    """
    lat1 = math.radians(node1.latitude)
    lat2 = math.radians(node2.latitude)
    delta_lat = math.radians(node2.latitude - node1.latitude)
    delta_long = math.radians(node2.longitude - node1.longitude)

    a = math.sin(delta_lat / 2) * math.sin(delta_lat / 2) + math.cos(lat1) * math.cos(lat2) * math.sin(delta_long / 2) * math.sin(delta_long / 2)
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))

    distance = EARTH_RADIUS * c

    return distance


def calculate_h(node):
    """
    计算节点到目标点的估价函数
    :param node: 节点
    :return: 估价函数值
    """
    goal_node = Node(GOAL_LATITUDE, GOAL_LONGITUDE, FLIGHT_HEIGHT)
    distance = get_distance(node, goal_node)

    return distance


def get_neighbors(node):
    """
    获取节点的相邻节点
    :param node: 节点
    :return: 相邻节点列表
    """
    neighbors = []

    for i in range(-1, 2):
        for j in range(-1, 2):
            if i == 0 and j == 0:
                continue

            latitude = node.latitude + (i * MINIMUM_DISTANCE) / EARTH_RADIUS * (180 / math.pi)
            longitude = node.longitude + (j * MINIMUM_DISTANCE) / EARTH_RADIUS * (180 / math.pi) / math.cos(node.latitude * math.pi / 180)
            height = node.height

            neighbor_node = Node(latitude, longitude, height)

            # 判断相邻节点是否在障碍物上
            obstacle_flag = False
            for obstacle in OBSTACLES:
                distance = get_distance(neighbor_node, Node(obstacle[0], obstacle[1], height))
                if distance < MINIMUM_DISTANCE:
                    obstacle_flag = True
                    break

            if obstacle_flag:
                continue

            # 判断相邻节点是否超出了地球表面
            if neighbor_node.latitude > 90 or neighbor_node.latitude < -90 or neighbor_node.longitude > 180 or neighbor_node.longitude < -180:
                continue

            # 判断相邻节点是否与父节点重合
            if node.parent is not None and node.parent == neighbor_node:
                continue

            neighbors.append(neighbor_node)

    return neighbors


def a_star(start_node, goal_node):
    """
    A星算法
    :param start_node: 起点节点
    :param goal_node: 目标节点
    :return: 路径列表
    """
    open_list = [start_node]
    closed_list = set()

    while open_list:
        current_node = min(open_list, key=lambda node: node.f)
        open_list.remove(current_node)
        closed_list.add(current_node)

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

        for neighbor_node in get_neighbors(current_node):
            if neighbor_node in closed_list:
                continue

            tentative_g = current_node.g + get_distance(current_node, neighbor_node) / FLIGHT_SPEED
            if neighbor_node not in open_list:
                open_list.append(neighbor_node)
            elif tentative_g >= neighbor_node.g:
                continue

            neighbor_node.parent = current_node
            neighbor_node.g = tentative_g
            neighbor_node.h = calculate_h(neighbor_node)
            neighbor_node.f = neighbor_node.g + neighbor_node.h

    return None


if __name__ == '__main__':
    start_node = Node(START_LATITUDE, START_LONGITUDE, FLIGHT_HEIGHT)
    goal_node = Node(GOAL_LATITUDE, GOAL_LONGITUDE, FLIGHT_HEIGHT)

    path = a_star(start_node, goal_node)

    if path is not None:
        for node in path:
            print(f"({node.latitude}, {node.longitude}, {node.height})")
    else:
        print("无法找到路径")

该代码实现了一个A星算法，用于在地球表面基于经纬度和高度以及大量约束条件下规划无人机的航路。其中，`Node`类表示一个节点，保存了节点的经纬度和高度信息，以及与该节点相关的一些算法参数。`get_distance`函数用于计算两个节点之间的距离。`calculate_h`函数用于计算一个节点到目标节点的估价函数。`get_neighbors`函数用于获取一个节点的相邻节点。`a_star`函数实现了A星算法，在起点和目标点之间寻找一条最优路径，并返回路径列表。最后，我们可以通过调用`a_star`函数来获取无人机的航路。