多无人机任务分配与路径规划 matlab程序

以下是一个简单的基于规划算法的多无人机任务分配与路径规划的 Matlab 程序：

% 多无人机任务分配与路径规划

% 初始化无人机数量和任务数量
num_drones = 3;
num_tasks = 5;

% 生成随机的无人机位置和任务位置
drone_pos = rand(num_drones, 2) * 100;
task_pos = rand(num_tasks, 2) * 100;

% 计算无人机和任务之间的距离
dist = zeros(num_drones, num_tasks);
for i = 1:num_drones
    for j = 1:num_tasks
        dist(i,j) = norm(drone_pos(i,:) - task_pos(j,:));
    end
end

% 设置参数
max_iter = 1000;
pop_size = 50;
mut_rate = 0.1;

% 初始化种群
pop = zeros(pop_size, num_tasks);
for i = 1:pop_size
    pop(i,:) = randperm(num_tasks);
end

% 迭代遗传算法
for iter = 1:max_iter
    
    % 计算种群适应度
    fitness = zeros(pop_size, 1);
    for i = 1:pop_size
        path = zeros(num_drones, num_tasks);
        for j = 1:num_drones
            path(j,:) = pop(i,:);
        end
        fitness(i) = calc_fitness(path, dist);
    end
    
    % 选择
    [val, idx] = sort(fitness, 'descend');
    parents = pop(idx(1:pop_size/2),:);
    
    % 交叉
    children = zeros(pop_size/2, num_tasks);
    for i = 1:pop_size/4
        p1 = parents(i*2-1,:);
        p2 = parents(i*2,:);
        c1 = zeros(1, num_tasks);
        c2 = zeros(1, num_tasks);
        idx = randperm(num_tasks, 2);
        idx = sort(idx);
        c1(idx(1):idx(2)) = p1(idx(1):idx(2));
        c2(idx(1):idx(2)) = p2(idx(1):idx(2));
        j = 1;
        for k = idx(2)+1:num_tasks
            while j <= num_tasks
                if ~ismember(p2(j), c1)
                    c1(k) = p2(j);
                    j = j + 1;
                    break;
                end
                j = j + 1;
            end
        end
        j = 1;
        for k = idx(2)+1:num_tasks
            while j <= num_tasks
                if ~ismember(p1(j), c2)
                    c2(k) = p1(j);
                    j = j + 1;
                    break;
                end
                j = j + 1;
            end
        end
        children(i*2-1,:) = c1;
        children(i*2,:) = c2;
    end
    
    % 变异
    for i = 1:pop_size/2
        if rand < mut_rate
            idx = randperm(num_tasks, 2);
            children(i,idx) = children(i,fliplr(idx));
        end
    end
    
    % 合并父代和子代
    pop = [parents; children];
    
end

% 找到最优解
path = zeros(num_drones, num_tasks);
for i = 1:num_drones
    path(i,:) = pop(1,:);
end

% 绘制路径
figure;
hold on;
for i = 1:num_drones
    plot(drone_pos(i,1), drone_pos(i,2), 'ro', 'MarkerSize', 10, 'LineWidth', 2);
    for j = 1:num_tasks
        if path(i,j) > 0
            plot([drone_pos(i,1) task_pos(path(i,j),1)], [drone_pos(i,2) task_pos(path(i,j),2)], 'b--');
            plot(task_pos(path(i,j),1), task_pos(path(i,j),2), 'bx', 'MarkerSize', 10, 'LineWidth', 2);
        end
    end
end
axis equal;
title('Multi-Drone Task Assignment and Path Planning');
xlabel('X (m)');
ylabel('Y (m)');

% 计算适应度函数
function fitness = calc_fitness(path, dist)
    num_drones = size(path, 1);
    num_tasks = size(path, 2);
    fitness = 0;
    for i = 1:num_drones
        for j = 1:num_tasks-1
            fitness = fitness + dist(i,path(i,j)) + dist(i,path(i,j+1));
        end
        fitness = fitness + dist(i,path(i,num_tasks));
    end
end

该程序使用遗传算法进行任务分配和路径规划，首先随机生成无人机位置和任务位置，然后计算无人机和任务之间的距离。接着，设置遗传算法的参数，初始化种群，然后进行迭代。在每次迭代中，计算种群适应度，进行选择、交叉和变异，最后找到最优解并绘制路径。适应度函数计算的是无人机飞行路径的总长度。