机器人路径规划仿真matlab

### 机器人路径规划仿真 MATLAB 实现教程

#### 基于DQN算法的单机器人路径规划

在MATLAB中实现基于深度Q网络(DQN)的机器人路径规划，可以按照如下方式构建模型：

% 初始化环境参数
envSize = [10, 10]; % 定义环境大小为10x10网格
startPos = [1, 1];   % 起始位置设为(1,1)
goalPos = [9, 9];    % 终点位置设为(9,9)

% 创建环境对象并设置障碍物
env = createEnvironment(envSize);
addObstacles(env); 

% 构建神经网络作为策略函数逼近器
net = createDeepQLearningNetwork();

% 设置训练选项
trainOpts = rlTrainingOptions('MaxEpisodes', 500,...
                              'StopOnError', true);

% 训练智能体直到收敛或达到最大迭代次数
doTrainAgent(net, env, trainOpts);

function doTrainAgent(network, environment, options)
    agent = rlDDPGAgent(network, getActionSpec(environment),...
                        getCriticOptimizerInfo(),getActorOptimizerInfo());
    
    experienceBuffer = [];
    episodeCount = 0;
    while ~isTerminated(options.MaxEpisodes, episodeCount)
        currentState = reset(environment);
        totalReward = 0;
        
        doneFlag = false;
        while ~doneFlag
            action = selectAction(agent, currentState);
            nextState = step(environment, action);
            
            reward = calculateReward(currentState, nextState, goalPos);
            addExperience(experienceBuffer, ...
                          Experience(currentState,action,reward,nextState));
            
            updatePolicyUsingReplayBuffer(agent,experienceBuffer);
            
            currentState = nextState;
            totalReward = totalReward + reward;
            doneFlag = isGoalReached(nextState, goalPos);
        end
        
        display(['Episode ', num2str(++episodeCount), ': Total Reward=',num2str(totalReward)]);
    end
    
end

上述代码展示了如何创建一个简单的二维离散空间内的导航场景，并利用DQN来学习从起点到终点的最佳行动序列[^1]。

对于多移动机器人的路径规划，则需考虑更多因素如避碰机制、通信协议以及协调控制等问题。一种常见方法是采用集中式管理架构，在此框架下设计全局优化目标函数，使得所有个体共同协作达成整体最优解；另一种则是分布式处理模式，让每台设备独立决策但保持局部交互以便同步信息交换[^3]。

#### 复杂环境下多机器人协同工作实例

当面对更复杂的任务需求时——比如存在多个动态障碍或是未知区域探索等情况，可借助深度确定性策略梯度（DDPG）这类连续动作空间下的强化学习技术来进行高效求解。下面给出一段简化版伪码用于说明这一过程:

for each robot r in robots
    initialize actor and critic networks N_r^μ ,N_r^Q with random weights w_μ,w_Q ;
repeat until convergence or timeout limit reached:
     for t=1 to T_max steps per epoch :
          observe current state s_t of all agents including self-position p_{r,t} relative positions q_{ij}, velocities v_i etc.;
          
          choose actions a_t according to stochastic policy π(a|s)=softmax(N_r^μ(s;w_μ)) plus some noise ε sampled from Ornstein-Uhlenbeck process;
          
          execute chosen actions simultaneously across entire fleet then receive immediate rewards R={r_k};
          
          store transition tuple (s_t,a_t,R,s_(t+1)) into replay memory buffer D;
          
          sample mini-batch B uniformly at random without replacement from D;
          
          perform gradient descent updates on both actors' parameters θ_μ using deterministic policy gradients rule ∇θJ≈E[(∇aQ(s,a|θ_Q)|a=π(s))^T (∇θπ(s))] evaluated over batch samples S∼B;
          
          apply delayed soft target network parameter updates τ<1 via polyak averaging scheme.

这段程序片段描述了一种适用于大规模群体行为模拟的学习流程，其中涉及到了状态观测、动作选取、即时反馈获取等多个环节的设计思路[^4]。