无人机多编队路径规划，自主避障
三维空间无人机集群编队控制，避障，目标追踪
无人机开发matlab仿真，支持多旋翼飞行器（包含四轴和六轴）、Ros无人机仿真，具有Slam导航、定位、路径规划、自主避障等功能。
ROS，Gazebo,PX4无人机仿真环境
Ubuntu18.04，实现圆轨迹、8轨迹飞行
无人机、无人车路径规划，避障，无人机集群飞行
① 四旋翼编队控制：包括目标分配、全局和局部路径规划
② 无多人机模拟复杂机制和动态行为
③ 单机模拟，路径跟随、规划；无人机群仿真控制

以下示例代码仅供参考

无人机多编队路径规划和自主避障是一个复杂的问题，涉及路径规划算法、避障策略以及编队控制。以下是一个简化的代码示例，使用 Python 和 ROS（Robot Operating System）环境来实现无人机的路径规划与避障功能。

使用以下技术：

1. 路径规划：A* 算法或 RRT（快速随机树）算法。
2. 避障：基于激光雷达或深度相机的动态避障。
3. 编队控制：简单的领导者-跟随者模型。

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环境准备

确保你已经安装了 ROS 和相关依赖库（如 rospy 和 tf）。以下代码是一个简化的示例，假设我们使用 Gazebo 模拟器进行仿真。

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代码实现

1. 路径规划 - A* 算法

以下是基于网格地图的 A* 算法实现：

import heapq

class Node:
    def __init__(self, position, parent=None):
        self.position = position
        self.parent = parent
        self.g = 0  # Cost from start to current node
        self.h = 0  # Heuristic cost to goal
        self.f = 0  # Total cost (g + h)

    def __lt__(self, other):
        return self.f < other.f

def astar(grid, start, goal):
    open_list = []
    closed_set = set()

    start_node = Node(start)
    goal_node = Node(goal)
    heapq.heappush(open_list, start_node)

    while open_list:
        current_node = heapq.heappop(open_list)
        closed_set.add(current_node.position)

        if current_node.position == goal_node.position:
            path = []
            while current_node:
                path.append(current_node.position)
                current_node = current_node.parent
            return path[::-1]

        neighbors = [(0, 1), (1, 0), (0, -1), (-1, 0)]  # 4-connected grid
        for dx, dy in neighbors:
            neighbor_pos = (current_node.position[0] + dx, current_node.position[1] + dy)

            if (
                neighbor_pos[0] < 0 or neighbor_pos[0] >= len(grid) or
                neighbor_pos[1] < 0 or neighbor_pos[1] >= len(grid[0]) or
                grid[neighbor_pos[0]][neighbor_pos[1]] == 1 or
                neighbor_pos in closed_set
            ):
                continue

            neighbor_node = Node(neighbor_pos, current_node)
            neighbor_node.g = current_node.g + 1
            neighbor_node.h = abs(neighbor_pos[0] - goal_node.position[0]) + abs(neighbor_pos[1] - goal_node.position[1])
            neighbor_node.f = neighbor_node.g + neighbor_node.h

            if any(neighbor_pos == n.position and neighbor_node.g >= n.g for n in open_list):
                continue

            heapq.heappush(open_list, neighbor_node)

    return None

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2. 避障 - 动态窗口法（DWA）

动态窗口法是一种常用的局部避障算法：

def dwa_control(x, goal, obstacles):
    # 参数设置
    max_speed = 1.0
    max_yaw_rate = 1.57
    dt = 0.1
    speed_resolution = 0.1
    yaw_rate_resolution = 0.1
    predict_time = 3.0

    def calc_dynamic_window(x):
        Vs = [0, max_speed, -max_yaw_rate, max_yaw_rate]
        Vd = [x[3] - speed_resolution, x[3] + speed_resolution,
              x[4] - yaw_rate_resolution, x[4] + yaw_rate_resolution]
        return [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]),
                max(Vs[2], Vd[2]), min(Vs[3], Vd[3])]

    def motion(x, u, dt):
        x[0] += u[0] * np.cos(x[2]) * dt
        x[1] += u[0] * np.sin(x[2]) * dt
        x[2] += u[1] * dt
        x[3] = u[0]
        x[4] = u[1]
        return x

    def calc_trajectory(x_init, v, w):
        x = np.array(x_init)
        trajectory = np.array(x)
        time = 0
        while time <= predict_time:
            x = motion(x, [v, w], dt)
            trajectory = np.vstack((trajectory, x))
            time += dt
        return trajectory

    def calc_obstacle_cost(trajectory, obstacles):
        for ox, oy in obstacles:
            dx = trajectory[:, 0] - ox
            dy = trajectory[:, 1] - oy
            dist = np.sqrt(dx**2 + dy**2)
            if np.min(dist) < 0.5:  # 安全距离
                return float('inf')
        return 0

    dw = calc_dynamic_window(x)
    best_u = [0, 0]
    best_trajectory = None
    min_cost = float('inf')

    for v in np.arange(dw[0], dw[1], speed_resolution):
        for w in np.arange(dw[2], dw[3], yaw_rate_resolution):
            trajectory = calc_trajectory(x, v, w)
            to_goal_cost = np.linalg.norm(trajectory[-1, :2] - goal[:2])
            ob_cost = calc_obstacle_cost(trajectory, obstacles)
            final_cost = to_goal_cost + ob_cost

            if final_cost < min_cost:
                min_cost = final_cost
                best_u = [v, w]
                best_trajectory = trajectory

    return best_u, best_trajectory

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3. 编队控制 - 领导者-跟随者模型

以下是一个简单的编队控制逻辑：

def formation_control(leader_position, follower_positions, desired_distances):
    control_commands = []
    for i, follower_pos in enumerate(follower_positions):
        desired_pos = leader_position + desired_distances[i]
        error = desired_pos - follower_pos
        velocity = error * 0.5  # 比例控制器
        control_commands.append(velocity)
    return control_commands

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整合到 ROS 中

将上述模块整合到 ROS 的节点中，并通过 rospy 发布控制命令和接收传感器数据。

import rospy
from geometry_msgs.msg import Twist, PoseStamped

def drone_controller():
    rospy.init_node('drone_controller', anonymous=True)
    pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)
    rate = rospy.Rate(10)

    while not rospy.is_shutdown():
        # 获取传感器数据并更新路径规划
        path = astar(grid_map, start, goal)
        velocity_command = dwa_control(current_state, goal, obstacles)

        # 发布控制命令
        cmd = Twist()
        cmd.linear.x = velocity_command[0]
        cmd.angular.z = velocity_command[1]
        pub.publish(cmd)
        rate.sleep()

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总结

以上代码展示了无人机多编队路径规划与自主避障的基本框架，包括路径规划（A*）、避障（DWA）和编队控制（领导者-跟随者模型）。在实际应用中，还需要结合具体硬件平台和传感器数据进行优化。