Deep Reinforcement Learning-Based Mapless Navigation for Mobile Robot in Unknown Environment With Local Optima

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Yiming Hu;Shuting Wang;Yuanlong Xie;Shiqi Zheng;Peng Shi;Imre Rudas;Xiang Cheng
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Abstract

Local optima issues challenge mobile robots mapless navigation with the dilemma of avoiding collisions and approaching the target. Planning-based methods rely on environmental models and manual strategies to guide the robot. In contrast, learning-based methods can process original sensor data to navigate the robot in real-time but struggle with local optima. To address this, we designed reward rules that punish the robot for previously visited areas that may trap the robot, and reward it for exploring local areas in diverse ways and escaping from local optima areas. Then, we improved the Soft Actor-Critic (SAC) algorithm by making its temperature parameter adaptive to the current training status, and memorizing it in experiences for strategy updating, bringing additional exploratory behaviors and necessary stability into the training. Finally, with the assistance of auxiliary networks, the robot learns to handle various navigation tasks with local optima risks. Simulations demonstrate the advantages of our method in terms of both success rate and path efficiency compared to several existing methods. Experiments verified the proposed method in real-world scenarios.
基于深度强化学习的局部最优未知环境下移动机器人无地图导航
局部最优问题给移动机器人的无地图导航带来了避免碰撞和接近目标的难题。基于规划的方法依赖于环境模型和人工策略来引导机器人。相比之下,基于学习的方法可以处理原始传感器数据来实时导航机器人,但会遇到局部最优问题。为了解决这个问题,我们设计了奖励规则,惩罚机器人之前访问过的可能困住机器人的区域,并奖励它以各种方式探索局部区域并从局部最优区域逃脱。然后,我们改进了软行为-评价(SAC)算法,使其温度参数自适应当前训练状态,并将其存储在经验中用于策略更新,为训练带来额外的探索行为和必要的稳定性。最后,在辅助网络的帮助下,机器人学习处理各种具有局部最优风险的导航任务。仿真结果表明,与现有的几种方法相比,该方法在成功率和路径效率方面都具有优势。实验验证了该方法的有效性。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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