维普中文期刊产品整合服务

A review of mobile robot motion planning methods:from classical motion planning workflows to reinforcement learning-based architectures

查看全文 作　　者：DONG [1]Lu;HE [2,3]Zichen;SONG [3]Chunwei;SUN [3,4]Changyin 高影响力作者 机构地区：[1]School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China;[2]Shanghai Institute of Intelligent Science and Technology,Tongji University,Shanghai 201804,China;[3]College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China;[4]School of Automation,Southeast University,Nanjing 210096,China高影响力机构 出　　处：《Journal of Systems Engineering and Electronics》索引2023年第34卷第2期,共21页高影响力期刊 基　　金：supported by the National Natural Science Foundation of China (62173251);the“Zhishan”Scholars Programs of Southeast University;the Fundamental Research Funds for the Central Universities;Shanghai Gaofeng&Gaoyuan Project for University Academic Program Development (22120210022) 摘　　要：Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research. 关 键 词：mobile robot reinforcement learning(RL) motion planning multi-robot cooperative planning
相关文献

参考文献(148)

引证文献(11)

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证 新出网证(渝)字10号 全国400电话 - 免长途话费