机械臂避障路径规划蚁群算法matlab完整代码

以下是机械臂避障路径规划蚁群算法的 MATLAB 完整代码：

clear all;
close all;
clc;

% 参数设置
start_point = [0, 0]; % 起点
end_point = [10, 10]; % 终点
obstacle = [4, 4]; % 障碍物
alpha = 1; % 信息素重要程度因子
beta = 5; % 启发函数重要程度因子
rho = 0.5; % 信息素挥发因子
Q = 1; % 常系数
ant_count = 50; % 蚂蚁数量
iter_max = 500; % 最大迭代次数
d = 2; % 坐标维数
t0 = 1; % 初始信息素浓度
eta = 1 / (norm(end_point - start_point)); % 启发函数

% 初始化信息素矩阵
tau = t0 * ones(d + 1, d + 1);
tau(end, :) = 0;
tau(:, end) = 0;

% 绘制地图
figure(1);
hold on;
grid on;
axis([0 12 0 12]);
plot(start_point(1), start_point(2), 'ro', 'MarkerSize', 10);
plot(end_point(1), end_point(2), 'ro', 'MarkerSize', 10);
plot(obstacle(1), obstacle(2), 's', 'MarkerSize', 10, 'MarkerFaceColor', 'r');
text(start_point(1) + 0.5, start_point(2) + 0.5, 'Start');
text(end_point(1) + 0.5, end_point(2) + 0.5, 'End');
text(obstacle(1) + 0.5, obstacle(2) + 0.5, 'Obstacle');
xlabel('X');
ylabel('Y');
title('Map');

% 开始迭代
for iter = 1:iter_max
    % 初始化蚂蚁位置
    ant_pos = repmat(start_point, ant_count, 1);
    ant_path = zeros(ant_count, d);
    ant_dist = zeros(ant_count, 1);
    
    % 每只蚂蚁搜索路径
    for k = 1:ant_count
        for i = 1:d
            % 计算可行方向的概率
            p = tau(ant_pos(k, i) + 1, :) .^ alpha .* eta .^ beta;
            p(ant_pos(k, i)) = 0;
            p(end) = 0;
            p = p / sum(p);
            
            % 选择下一个位置
            next_pos = find(rand < cumsum(p), 1) - 1;
            if next_pos == d
                break;
            end
            
            % 更新路径和距离
            ant_path(k, i) = next_pos;
            ant_dist(k) = ant_dist(k) + norm(ant_pos(k, :) - ant_path(k, :));
            ant_pos(k, i + 1) = next_pos;
        end
    end
    
    % 计算每只蚂蚁的路径信息素贡献
    delta_tau = zeros(d + 1, d + 1);
    for k = 1:ant_count
        if any(ant_path(k, :) == obstacle)
            continue;
        end
        for i = 1:d
            delta_tau(ant_pos(k, i) + 1, ant_path(k, i) + 1) = delta_tau(ant_pos(k, i) + 1, ant_path(k, i) + 1) + Q / ant_dist(k);
        end
        delta_tau(end, ant_pos(k, end) + 1) = delta_tau(end, ant_pos(k, end) + 1) + Q / ant_dist(k);
    end
    
    % 更新信息素
    tau = (1 - rho) * tau + delta_tau;
    
    % 更新启发函数
    eta = 1 / (norm(end_point - start_point) + iter * ant_dist(1));
    
    % 绘制路径
    if mod(iter, 10) == 0
        figure(1);
        plot(start_point(1), start_point(2), 'ro', 'MarkerSize', 10);
        plot(end_point(1), end_point(2), 'ro', 'MarkerSize', 10);
        plot(obstacle(1), obstacle(2), 's', 'MarkerSize', 10, 'MarkerFaceColor', 'r');
        text(start_point(1) + 0.5, start_point(2) + 0.5, 'Start');
        text(end_point(1) + 0.5, end_point(2) + 0.5, 'End');
        text(obstacle(1) + 0.5, obstacle(2) + 0.5, 'Obstacle');
        xlabel('X');
        ylabel('Y');
        title(['Iteration ' num2str(iter)]);
        for k = 1:ant_count
            if any(ant_path(k, :) == obstacle)
                continue;
            end
            line([start_point(1), ant_pos(k, 1)], [start_point(2), ant_pos(k, 2)], 'Color', [0.5, 0.5, 0.5], 'LineWidth', 1);
            for i = 1:d-1
                line([ant_pos(k, i), ant_path(k, i)], [ant_pos(k, i+1), ant_path(k, i+1)], 'Color', [0.5, 0.5, 0.5], 'LineWidth', 1);
            end
            line([ant_path(k, d), end_point(1)], [ant_path(k, d+1), end_point(2)], 'Color', [0.5, 0.5, 0.5], 'LineWidth', 1);
        end
    end
    
    % 判断是否到达终点
    if any(ant_path(:, end) == end_point)
        disp(['Iteration ' num2str(iter) ': Reach the end point!']);
        break;
    end
end

希望能对你有所帮助！