用matlab程序写出使用人工蜂群算法实现的机器人路径规划问题的代码

以下是使用人工蜂群算法实现机器人路径规划问题的MATLAB代码：

% 机器人路径规划问题的人工蜂群算法实现
% 作者：AI学园
% 时间：2021.9.29

clc; clear; close all;

%% 参数设置
% 目标点坐标
goal = [100, 100];

% 路径规划区域边界
boundary = [0, 0, 200, 200];

% 障碍物坐标
obs = [60, 90, 20, 20;
       80, 130, 20, 20;
       120, 50, 20, 20;
       150, 120, 20, 20];

% 机器人初始位置
start = [20, 20];

% 最大迭代次数
max_iter = 500;

% 种群大小
pop_size = 30;

% 蜜蜂数量
bee_num = round(pop_size/2);

% 路径长度惩罚系数
penalty = 50;

% 路径可行性检测阈值
threshold = 40;

% 蜜蜂搜索半径
radius = 15;

% 初始化种群
pop = repmat(start, pop_size, 1);
pop_fitness = zeros(pop_size, 1);

% 统计最优解
global_best = start;
global_best_fitness = inf;

%% 蜜蜂搜索
for iter = 1 : max_iter
    % 招聘蜜蜂搜索
    for i = 1 : bee_num
        % 随机选择一个个体
        k = randi(pop_size);
        while k == i
            k = randi(pop_size);
        end
        
        % 生成随机解
        new_solution = pop(i, :) + radius * (2*rand(1, 2) - 1);
        
        % 解约束处理
        new_solution = max(new_solution, boundary(1:2));
        new_solution = min(new_solution, boundary(3:4));
        
        % 计算新解适应度
        new_fitness = fitness_func(new_solution, obs, penalty, threshold);
        
        % 判断是否更新个体最优解
        if new_fitness < pop_fitness(i)
            pop(i, :) = new_solution;
            pop_fitness(i) = new_fitness;
        end
        
        % 判断是否更新全局最优解
        if new_fitness < global_best_fitness
            global_best = new_solution;
            global_best_fitness = new_fitness;
        end
    end
    
    % 侦查蜜蜂搜索
    for i = bee_num+1 : pop_size
        % 生成随机解
        new_solution = boundary(1:2) + (boundary(3:4)-boundary(1:2)) .* rand(1, 2);
        
        % 计算新解适应度
        new_fitness = fitness_func(new_solution, obs, penalty, threshold);
        
        % 判断是否更新个体最优解
        if new_fitness < pop_fitness(i)
            pop(i, :) = new_solution;
            pop_fitness(i) = new_fitness;
        end
        
        % 判断是否更新全局最优解
        if new_fitness < global_best_fitness
            global_best = new_solution;
            global_best_fitness = new_fitness;
        end
    end
    
    % 输出迭代结果
    disp(['迭代次数：', num2str(iter), '，最优解适应度：', num2str(global_best_fitness)]);
    
    % 绘制搜索过程
    figure(1);
    plot(start(1), start(2), 'ro', 'MarkerSize', 10, 'LineWidth', 2);
    hold on;
    plot(goal(1), goal(2), 'go', 'MarkerSize', 10, 'LineWidth', 2);
    plot(obs(:,1)+obs(:,3)/2, obs(:,2)+obs(:,4)/2, 'ks', 'MarkerSize', 10, 'LineWidth', 2);
    plot(global_best(1), global_best(2), 'b*', 'MarkerSize', 10, 'LineWidth', 2);
    plot(pop(:,1), pop(:,2), 'y.', 'MarkerSize', 10);
    axis([boundary(1)-10, boundary(3)+10, boundary(2)-10, boundary(4)+10]);
    xlabel('X坐标');
    ylabel('Y坐标');
    title(['迭代次数：', num2str(iter), '，最优解适应度：', num2str(global_best_fitness)]);
    hold off;
end

%% 最优路径绘制
path = [start; global_best];
while norm(path(end,:) - goal) > 0.1
    % 生成新解
    new_solution = path(end, :) + 0.5 * (goal - path(end, :)) + 5 * (2*rand(1,2)-1);
    
    % 解约束处理
    new_solution = max(new_solution, boundary(1:2));
    new_solution = min(new_solution, boundary(3:4));
    
    % 计算新解适应度
    new_fitness = fitness_func(new_solution, obs, penalty, threshold);
    
    % 判断是否更新解
    if new_fitness < global_best_fitness
        path(end+1, :) = new_solution;
        global_best_fitness = new_fitness;
    end
end

% 绘制最优路径
figure(2);
plot(start(1), start(2), 'ro', 'MarkerSize', 10, 'LineWidth', 2);
hold on;
plot(goal(1), goal(2), 'go', 'MarkerSize', 10, 'LineWidth', 2);
plot(obs(:,1)+obs(:,3)/2, obs(:,2)+obs(:,4)/2, 'ks', 'MarkerSize', 10, 'LineWidth', 2);
plot(path(:,1), path(:,2), 'r-', 'LineWidth', 2);
axis([boundary(1)-10, boundary(3)+10, boundary(2)-10, boundary(4)+10]);
xlabel('X坐标');
ylabel('Y坐标');
title('机器人路径规划结果');
hold off;

%% 适应度函数
function fitness = fitness_func(solution, obs, penalty, threshold)
    % 路径长度计算
    dist = norm(solution - obs(1,1:2));
    for i = 2 : size(obs, 1)
        dist = dist + norm(solution - obs(i,1:2));
    end
    dist = dist + norm(solution - [100, 100]);
    
    % 路径可行性检测
    for i = 1 : size(obs, 1)
        if norm(solution - obs(i,1:2)) < threshold
            fitness = inf;
            return;
        end
    end
    
    % 计算适应度
    fitness = dist + penalty * size(obs, 1);
end

代码实现了机器人路径规划问题的人工蜂群算法，其中使用了适应度函数对路径长度和可行性进行评价。在每次迭代中，先进行招聘蜜蜂搜索，然后进行侦查蜜蜂搜索，最后统计最优解。最优路径的绘制采用了贪心策略生成新解，直到达到目标点为止。