华中科技大学学报（自然科学版）2026，Vol.54Issue(5)：98-104,7.DOI:10.13245/j.hust.240408
基于自适应熵值的移动机器人无地图导航方法
Mapless navigation method for mobile robots based on adaptive-entropy
摘要
Abstract
Aiming at the problem of poor ability to escape from local optima after a robot became trapped,which existed in existing mapless navigation methods based on machine learning,an adaptive entropy mapless navigation method was proposed.First,this method determined whether a local optimum phenomenon occurred according to the reward fluctuations during the robot's training process,and then on this basis,continued to improve the entropy adjustment mechanism in the soft actor-critic(SAC)algorithm to achieve the effect of adaptively changing the temperature coefficient to strengthen its learning ability after the robot became trapped in a local optimum,and finally promoted the robot to successfully learn a globally optimal navigation strategy.Simulation results show that compared with existing methods,the navigation strategy trained by the proposed method has stronger escape ability and higher navigation success rate.关键词
移动机器人/无地图导航/深度强化学习/局部最优/SAC算法Key words
mobile robots/mapless navigation/deep reinforcement learning/local optima/soft actor-critic(SAC)algorithm分类
信息技术与安全科学引用本文复制引用
熊体凡,李泓辰,王书亭,谢远龙,胡倚铭..基于自适应熵值的移动机器人无地图导航方法[J].华中科技大学学报（自然科学版）,2026,54(5):98-104,7.基金项目
国家自然科学基金资助项目(52275488). （52275488）
