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

Motion planning is critical to realize the autono-mous operation of mobile robots. As the complexity and ran-domness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is chal-lenged. With the development of machine learning,the deep reinforcement learning (DRL)-based motion planner has gradually become a research hotspot due to its several advanta-geous 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 pro-vide 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 com-bined with RL improvements,map-free RL-based motion plan-ners,and multi-robot cooperative planning methods. Finally,we analyze the urgent challenges faced by these mainstream RL-based motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.