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

作　　者：DONG Lu HE Zichen SONG Chunwei SUN Changyin 

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

出　　处：《Journal of Systems Engineering and Electronics》2023年第2期439-459,共21页系统工程与电子技术（英文版）

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 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP181[自动化与计算机技术—控制科学与工程]