多无人机任务分配与路径规划 matlab程序
时间: 2023-08-02 20:06:33 浏览: 99
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:
```matlab
% 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。
阅读全文