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 autonomous operation of mobile robots. As the complexity and stochasticity of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. In recent years, with the development of intelligent computation technology, the deep reinforcement learning (DRL) based motion planning algorithm has gradually become a research hotspot due to its advantageous features such as not relying on the map prior, model-free, and unified global and local planning paradigms. In this paper, we provide a systematic review of various motion planning methods. First, we summarize the representative and cutting-edge algorithms for each submodule of the classical motion planning architecture, and analyze their performance limitations. For the rest part, we focus on reviewing RL-based motion planning approaches, including RL optimization motion planners, map-free end-to-end methods that integrate sensing and decision-making, and multi-robot cooperative planning methods. In the last section, we analyze the urgent challenges faced by these methods in detail, review some state-of-the-art works for these issues, and propose suggestions for future research.