机器人路径规划与避障避碰算法实现

1. 基于状态改变的路径规划算法

这种算法借鉴电路中元件状态改变的现象，将动态障碍物类比为外部激励，通过实时更新机器人的状态来实现避障。

% 参数设置
startPos = [0, 0]; % 起始位置
goalPos = [10, 10]; % 目标位置
obstacles = [3, 3, 1; 7, 7, 1]; % 障碍物位置和半径
robotRadius = 0.5; % 机器人半径
stepSize = 0.1; % 每步移动距离

% 初始化路径
path = startPos;
currentPos = startPos;

% 路径规划主循环
while norm(currentPos - goalPos) > robotRadius
    % 计算目标方向向量
    goalVector = goalPos - currentPos;
    goalVector = goalVector / norm(goalVector);
    
    % 检测障碍物
    for i = 1:size(obstacles, 1)
        obsPos = obstacles(i, 1:2);
        obsRadius = obstacles(i, 3);
        distance = norm(currentPos - obsPos);
        if distance < obsRadius + robotRadius
            % 计算避障方向
            obsVector = currentPos - obsPos;
            obsVector = obsVector / norm(obsVector);
            goalVector = obsVector; % 临时将避障方向作为目标方向
        end
    end
    
    % 更新位置
    currentPos = currentPos + stepSize * goalVector;
    path = [path; currentPos];
end

% 绘制路径
figure;
plot(path(:,1), path(:,2), 'b', 'LineWidth', 2);
hold on;
plot(startPos(1), startPos(2), 'go', 'MarkerSize', 10); % 起始点
plot(goalPos(1), goalPos(2), 'ro', 'MarkerSize', 10); % 目标点
for i = 1:size(obstacles, 1)
    viscircles(obstacles(i, 1:2), obstacles(i, 3), 'EdgeColor', 'k'); % 障碍物
end
axis equal;
xlabel('X');
ylabel('Y');
title('基于状态改变的路径规划');

2. 动态窗口算法（DWA）

动态窗口算法是一种基于采样和动态规划的局部路径规划方法，适用于动态环境。

% 参数设置
startPos = [0, 0]; % 起始位置
goalPos = [10, 10]; % 目标位置
obstacles = [3, 3, 1; 7, 7, 1]; % 障碍物位置和半径
robotRadius = 0.5; % 机器人半径
stepSize = 0.1; % 每步移动距离
velocityRange = [-1, 1]; % 速度范围
angularVelocityRange = [-pi/4, pi/4]; % 角速度范围
predictionTime = 5; % 预测时间

% 初始化路径
path = startPos;
currentPos = startPos;
currentAngle = 0;

% 路径规划主循环
while norm(currentPos - goalPos) > robotRadius
    % 生成候选速度
    velocities = linspace(velocityRange(1), velocityRange(2), 10);
    angularVelocities = linspace(angularVelocityRange(1), angularVelocityRange(2), 10);
    
    bestScore = -Inf;
    bestVelocity = 0;
    bestAngularVelocity = 0;
    
    % 评估每个候选速度
    for v = velocities
        for w = angularVelocities
            % 预测位置
            predictedPos = currentPos;
            predictedAngle = currentAngle;
            for t = 1:predictionTime
                predictedPos = predictedPos + [v * cos(predictedAngle), v * sin(predictedAngle)];
                predictedAngle = predictedAngle + w;
                
                % 检测碰撞
                collision = false;
                for i = 1:size(obstacles, 1)
                    obsPos = obstacles(i, 1:2);
                    obsRadius = obstacles(i, 3);
                    if norm(predictedPos - obsPos) < obsRadius + robotRadius
                        collision = true;
                        break;
                    end
                end
                
                if collision
                    break;
                end
            end
            
            if ~collision
                % 计算评分
                goalVector = goalPos - predictedPos;
                goalVector = goalVector / norm(goalVector);
                score = dot(goalVector, [cos(predictedAngle), sin(predictedAngle)]);
                if score > bestScore
                    bestScore = score;
                    bestVelocity = v;
                    bestAngularVelocity = w;
                end
            end
        end
    end
    
    % 更新位置和角度
    currentPos = currentPos + [bestVelocity * cos(currentAngle), bestVelocity * sin(currentAngle)];
    currentAngle = currentAngle + bestAngularVelocity;
    path = [path; currentPos];
end

% 绘制路径
figure;
plot(path(:,1), path(:,2), 'b', 'LineWidth', 2);
hold on;
plot(startPos(1), startPos(2), 'go', 'MarkerSize', 10); % 起始点
plot(goalPos(1), goalPos(2), 'ro', 'MarkerSize', 10); % 目标点
for i = 1:size(obstacles, 1)
    viscircles(obstacles(i, 1:2), obstacles(i, 3), 'EdgeColor', 'k'); % 障碍物
end
axis equal;
xlabel('X');
ylabel('Y');
title('动态窗口算法路径规划');

3. 基于运动显著性的动态环境避碰方法

这种方法利用动态物体的运动显著性信息，通过B样条插值方法预测动态物体的未来运动状态，并结合非线性模型预测控制（NMPC）实现避障。

% 参数设置
startPos = [0, 0]; % 起始位置
goalPos = [10, 10]; % 目标位置
dynamicObstacles = [3, 3, 1, [0.1, 0.1]; 7, 7, 1, [-0.1, -0.1]]; % 动态障碍物位置、半径和速度
robotRadius = 0.5; % 机器人半径
stepSize = 0.1; % 每步移动距离
predictionTime = 5; % 预测时间

% 初始化路径
path = startPos;
currentPos = startPos;

% 路径规划主循环
while norm(currentPos - goalPos) > robotRadius
    % 预测动态障碍物位置
    predictedObstacles = dynamicObstacles;
    for i = 1:size(dynamicObstacles, 1)
        predictedObstacles(i, 1:2) = predictedObstacles(i, 1:2) + predictedObstacles(i, 4:5) * predictionTime;
    end
    
    % 计算目标方向向量
    goalVector = goalPos - currentPos;
    goalVector = goalVector / norm(goalVector);
    
    % 检测碰撞
    collision = false;
    for i = 1:size(predictedObstacles, 1)
        obsPos = predictedObstacles(i, 1:2);
        obsRadius = predictedObstacles(i, 3);
        if norm(currentPos - obsPos) < obsRadius + robotRadius
            collision = true;
            break;
        end
    end
    
    if collision
        % 计算避障方向
        obsVector = currentPos - obsPos;
        obsVector = obsVector / norm(obsVector);
        goalVector = obsVector; % 临时将避障方向作为目标方向
    end
    
    % 更新位置
    currentPos = currentPos + stepSize * goalVector;
    path = [path; currentPos];
end

% 绘制路径
figure;
plot(path(:,1), path(:,2), 'b', 'LineWidth', 2);
hold on;
plot(startPos(1), startPos(2), 'go', 'MarkerSize', 10); % 起始点
plot(goalPos(1), goalPos(2), 'ro', 'MarkerSize', 10); % 目标点
for i = 1:size(dynamicObstacles, 1)
    viscircles(dynamicObstacles(i, 1:2), dynamicObstacles(i, 3), 'EdgeColor', 'k'); % 动态障碍物
end
axis equal;
xlabel('X');
ylabel('Y');
title('基于运动显著性的动态环境避碰');

参考代码 机器人的路径规划，实现避障避碰 www.youwenfan.com/contentcnk/101153.html