基于MATLAB的多机器人编队控制系统设计与实现

一、系统架构设计

1. 模块化架构

2. 核心组件

• 编队策略模块：支持动态切换领导跟随/虚拟结构/势场控制
• 运动控制模块：包含动力学模型与PID控制器
• 环境感知模块：集成激光雷达与视觉SLAM
• 通信系统：基于ROS的分布式通信架构

---

二、关键算法实现

1. 多模式编队控制

classdef FormationControl
    methods
        function initialize(obj, num_robots)
            obj.robots = repmat(struct('pos',[0,0],'vel',[0,0]), num_robots,1);
            obj.leader = 1; % 默认第一个为领导者
        end
        
        function updateFormation(obj, target)
            switch obj.strategy
                case 'leader-follower'
                    obj.leaderFollowerUpdate(target);
                case 'virtualStructure'
                    obj.virtualStructureUpdate(target);
                case 'potentialField'
                    obj.potentialFieldUpdate(target);
            end
        end
        
        function leaderFollowerUpdate(obj, target)
            % 领导者运动学模型
            leader = obj.robots(obj.leader);
            leader.vel = leader.vel + 0.1*(target - leader.pos);
            leader.pos = leader.pos + leader.vel*0.1;
            
            % 跟随者控制
            for i = 1:numel(obj.robots)
                if i ~= obj.leader
                    follower = obj.robots(i);
                    error = leader.pos - follower.pos;
                    follower.vel = follower.vel + 0.5*error;
                    follower.pos = follower.pos + follower.vel*0.1;
                end
            end
        end
    end
end

2. 动态队形生成

function positions = generateFormation(num_robots, formationType)
    switch formationType
        case 'circle'
            theta = linspace(0,2*pi,num_robots+1);
            positions = [cos(theta)', sin(theta)'] * 2;
        case 'line'
            positions = linspace(-3,3,num_robots)';
            positions = [positions, zeros(num_robots,1)];
        case 'diamond'
            positions = [3,0; 0,3; -3,0; 0,-3];
        otherwise
            error('Unknown formation type');
    end
end

3. 避障与编队保持

function force = computeForces(robot, others, obstacles)
    % 人工势场力计算
    attract = 0.5*(robot.target - robot.pos);
    
    repel = [0,0];
    for i = 1:numel(obstacles)
        d = norm(robot.pos - obstacles(i).pos);
        if d < 1.0
            repel = repel + 2*(1/d - 1/1.0)*(robot.pos - obstacles(i).pos)/d^3;
        end
    end
    
    % 机器人间排斥力
    for i = 1:numel(others)
        if i ~= robot.id
            d = norm(robot.pos - others(i).pos);
            if d < 0.8
                repel = repel + 1.5*(1/d - 1/0.8)*(robot.pos - others(i).pos)/d^3;
            end
        end
    end
    
    force = attract + repel;
end

---

三、可视化与交互

1. 实时仿真界面

function visualizeFormation(robots, obstacles)
    clf;
    hold on;
    
    % 绘制障碍物
    for i = 1:numel(obstacles)
        plot(obstacles(i).pos(1), obstacles(i).pos(2), 'ks', 'MarkerSize',10);
    end
    
    % 绘制机器人
    colors = hsv(numel(robots));
    for i = 1:numel(robots)
        plot(robots(i).pos(1), robots(i).pos(2), 'o', ...
            'Color', colors(i,:), 'MarkerSize',10, 'LineWidth',2);
        quiver(robots(i).pos(1), robots(i).pos(2), ...
            robots(i).vel(1), robots(i).vel(2), 1.5, 'r');
    end
    
    axis equal;
    grid on;
    xlim([-5,5]);
    ylim([-5,5]);
    drawnow;
end

2. 交互式控制面板

function setupUI()
    h = uicontrol('Style','pushbutton','String','Start Simulation',...
        'Position',[20 20 100 30],'Callback',@startSim);
    
    formationType = uicontrol('Style','popupmenu',...
        'String',{'Circle','Line','Diamond'},...
        'Position',[150 20 100 30]);
    
    numRobots = uicontrol('Style','edit',...
        'String','5',...
        'Position',[250 20 50 30]);
end

function startSim(~,~)
    num = str2double(get(findobj('String','5'),'String'));
    type = get(findobj('String',{'Circle','Line','Diamond'}),'Value');
    formationType = {'circle','line','diamond'}(type);
    
    % 初始化系统
    sys = FormationControl();
    sys.initialize(num);
    sys.strategy = formationType;
    
    % 主循环
    while true
        target = getTargetPosition(); % 获取目标位置
        sys.updateFormation(target);
        visualizeFormation(sys.robots, sys.obstacles);
        if checkCompletion(sys.robots, target)
            break;
        end
    end
end

---

四、优化

1. 分布式计算架构

   % 使用parfor实现并行计算
   parfor i = 1:num_robots
       robots(i).vel = computeControl(i, robots, obstacles);
   end
   

2. GPU加速

   % 将位置数据传输到GPU
   gpu_positions = gpuArray(robots.pos);
   
   % 并行计算排斥力场
   repel_forces = arrayfun(@(i) computeRepulsion(gpu_positions(i,:), obstacles), 1:num_robots);
   

3. 自适应控制参数

   function kp = adaptiveGain(error)
       base_gain = 0.5;
       error_threshold = 0.1;
       if error > error_threshold
           kp = base_gain * 2;
       else
           kp = base_gain;
       end
   end
   

---

五、工程应用案例

1. 仓储物流集群 5台AGV形成环形编队完成货架巡检 动态避障算法处理货架间通道障碍 实现98%的任务完成率
2. 灾害救援集群 3台机器人组成三角编队进入废墟 基于声呐的实时环境感知 通过中继通信保持编队连接
3. 农业喷洒集群 10台无人机形成菱形编队 基于视觉的作物行识别 保持2m间距的精准喷洒

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六、参考

1. MATLAB工具包 Robotics System Toolbox Reinforcement Learning Toolbox Phased Array System Toolbox
2. 代码 用于多机器人的编队控制，能够形成不同的编队 www.youwenfan.com/contentcnj/63780.html
3. 参考文献 《多机器人协同控制》（王飞跃） IEEE Transactions on Robotics 2023