  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, deep reinforcement
learning (DRL)-based motion planner has gradually become a research hotspot due
to its several advantageous features. DRL-based motion planner is model-free
and does not rely on the prior structured map. Most importantly, 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. First, we summarize the representative and state-of-the-art
works for each submodule of the classical motion planning architecture and
analyze their performance features. Subsequently, we concentrate on summarizing
RL-based motion planning approaches, including motion planners combined with RL
improvements, map-free RL-based motion planners, and multi-robot cooperative
planning methods. Last but not least, 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.
