无人机集群避障、多智能体协同控制、路径规划的matlab代码

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无人机集群避障、多智能体协同控制、路径规划的matlab代码
一共三个代码：
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
在这里插入图片描述

文章目录

以下是一个分步骤的MATLAB代码实现，涵盖无人机集群避障、多智能体协同控制和路径规划的三个主要任务。每个任务分别对应一个代码文件。

代码 ①：四旋翼编队控制

包括目标分配、全局路径规划和局部路径规划。

% 四旋翼编队控制：目标分配与路径规划
function QuadrotorFormationControl()
    % 初始化参数
    numUAVs = 4; % 无人机数量
    targetPositions = [10, 10, 5; 20, 10, 5; 10, 20, 5; 20, 20, 5]; % 目标位置
    initialPositions = [0, 0, 0; 5, 0, 0; 0, 5, 0; 5, 5, 0]; % 初始位置
    
    % 分配目标（简单的最近邻分配）
    assignedTargets = assignTargets(initialPositions, targetPositions);
    
    % 全局路径规划（直线路径）
    globalPaths = planGlobalPaths(initialPositions, assignedTargets);
    
    % 局部路径规划（动态避障）
    localPaths = cell(numUAVs, 1);
    for i = 1:numUAVs
        localPaths{i} = planLocalPath(globalPaths{i}, []);
    end
    
    % 可视化结果
    visualizePaths(initialPositions, assignedTargets, globalPaths, localPaths);
end

% 目标分配函数
function assignedTargets = assignTargets(initialPositions, targetPositions)
    numUAVs = size(initialPositions, 1);
    assignedTargets = zeros(size(initialPositions));
    remainingTargets = targetPositions;
    
    for i = 1:numUAVs
        [minDist, idx] = min(pdist2(initialPositions(i, :), remainingTargets));
        assignedTargets(i, :) = remainingTargets(idx, :);
        remainingTargets(idx, :) = [];
    end
end

% 全局路径规划函数
function paths = planGlobalPaths(initialPositions, targetPositions)
    numUAVs = size(initialPositions, 1);
    paths = cell(numUAVs, 1);
    for i = 1:numUAVs
        paths{i} = [initialPositions(i, :); targetPositions(i, :)];
    end
end

% 局部路径规划函数
function localPath = planLocalPath(globalPath, obstacles)
    % 简单的局部路径规划：直接返回全局路径
    % （可以扩展为A*算法或RRT算法）
    localPath = globalPath;
end

% 可视化路径
function visualizePaths(initialPositions, assignedTargets, globalPaths, localPaths)
    figure;
    hold on;
    grid on;
    xlabel('X');
    ylabel('Y');
    zlabel('Z');
    title('Quadrotor Formation Control');
    
    % 绘制初始位置和目标位置
    plot3(initialPositions(:, 1), initialPositions(:, 2), initialPositions(:, 3), 'ro', 'MarkerSize', 10, 'LineWidth', 2);
    plot3(assignedTargets(:, 1), assignedTargets(:, 2), assignedTargets(:, 3), 'go', 'MarkerSize', 10, 'LineWidth', 2);
    
    % 绘制全局路径
    for i = 1:length(globalPaths)
        path = globalPaths{i};
        plot3(path(:, 1), path(:, 2), path(:, 3), 'b-', 'LineWidth', 2);
    end
    
    % 绘制局部路径
    for i = 1:length(localPaths)
        path = localPaths{i};
        plot3(path(:, 1), path(:, 2), path(:, 3), 'm--', 'LineWidth', 2);
    end
    legend('Initial Positions', 'Target Positions', 'Global Paths', 'Local Paths');
end

代码 ②：无人机模拟复杂机制和动态行为

% 模拟无人机群复杂机制和动态行为
function MultiAgentSimulation()
    % 参数初始化
    numUAVs = 6; % 无人机数量
    positions = rand(numUAVs, 3) * 100; % 随机初始位置
    velocities = zeros(numUAVs, 3); % 初始速度
    dt = 0.1; % 时间步长
    totalTime = 20; % 总时间
    timeSteps = round(totalTime / dt);
    
    % 动态行为更新规则
    for t = 1:timeSteps
        % 更新速度和位置（基于邻居的平均位置和速度）
        for i = 1:numUAVs
            neighbors = findNeighbors(positions, i, 10); % 10米内的邻居
            avgPosition = mean(positions(neighbors, :), 1);
            avgVelocity = mean(velocities(neighbors, :), 1);
            
            % 使用简单规则更新速度
            velocities(i, :) = 0.9 * velocities(i, :) + ...
                               0.1 * (avgPosition - positions(i, :)) + ...
                               0.1 * (avgVelocity - velocities(i, :));
            
            % 更新位置
            positions(i, :) = positions(i, :) + velocities(i, :) * dt;
        end
        
        % 可视化当前状态
        clf;
        plot3(positions(:, 1), positions(:, 2), positions(:, 3), 'bo', 'MarkerSize', 10);
        xlabel('X');
        ylabel('Y');
        zlabel('Z');
        xlim([0, 100]);
        ylim([0, 100]);
        zlim([0, 100]);
        title(['Time Step: ', num2str(t)]);
        drawnow;
        pause(0.05);
    end
end

% 找到邻居
function neighbors = findNeighbors(positions, idx, radius)
    distances = sqrt(sum((positions - positions(idx, :)).^2, 2));
    neighbors = find(distances <= radius & distances > 0);
end

代码 ③：单机模拟，路径跟随与规划；无人机群仿真控制

% 单机路径规划与跟随
function SingleUAVPathFollowing()
    % 参数初始化
    waypoints = [0, 0, 0; 10, 10, 5; 20, 10, 10; 30, 30, 5]; % 路径点
    currentPosition = waypoints(1, :); % 初始位置
    currentVelocity = [0, 0, 0]; % 初始速度
    dt = 0.1; % 时间步长
    totalTime = 20; % 总时间
    timeSteps = round(totalTime / dt);
    
    % 路径跟随逻辑
    for t = 1:timeSteps
        % 获取目标点
        targetWaypoint = getNextWaypoint(waypoints, currentPosition);
        
        % 计算速度变化（PID控制器示例）
        error = targetWaypoint - currentPosition;
        currentVelocity = currentVelocity + 0.1 * error;
        
        % 更新位置
        currentPosition = currentPosition + currentVelocity * dt;
        
        % 可视化
        clf;
        plot3(waypoints(:, 1), waypoints(:, 2), waypoints(:, 3), 'r-o', 'LineWidth', 2);
        hold on;
        plot3(currentPosition(1), currentPosition(2), currentPosition(3), 'bo', 'MarkerSize', 10);
        xlabel('X');
        ylabel('Y');
        zlabel('Z');
        xlim([0, 40]);
        ylim([0, 40]);
        zlim([0, 15]);
        title(['Time Step: ', num2str(t)]);
        drawnow;
        pause(0.05);
    end
end

% 获取下一个路径点
function targetWaypoint = getNextWaypoint(waypoints, currentPosition)
    distances = sqrt(sum((waypoints - currentPosition).^2, 2));
    [~, idx] = min(distances);
    targetWaypoint = waypoints(idx, :);
end