基于深度强化算法的机器人动态目标点跟随研究

Dynamic Target Following Based on Reinforcement Learning of Robot-car

机器人的路径规划一直是机器人运动控制研究的热点。目前的路径规划需要耗费大量时间来构建地图,而基于不断“试错”机制的强化学习通过预先的训练可以实现无地图条件下的路径规划。通过对当前的多种深度强化学习算法进行研究和分析,利用低维度的雷达数据和少量位置信息,最终确定了在不同智能家居环境下的有效动态目标点跟踪策略,同时完成了避障功能。实验结果表明,基于优先采样的DQN、Dueling Double DQN和DDPG算法,在不同环境下呈现较强的泛化能力。

Robot path planning has always been a hot topic in robot motion control.The current path planning takes a lot of time to build the map,but the reinforcement learning based on continuous“trial and error”mechanism can realize the mapless navigation.Through the research and analysis of current various deep reinforcement learning algorithms,using low-dimensional radar data and a small amount of position information can follow a moving target and avoid collisions in indoor environments.The results show that DQN、Dueling Double DQN and DDPG algorithms based on priority sampling present strong generalization capabilities in different environment.