Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Abstract

Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motion-planning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actor-critic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

Abstract of "Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review" by Huihui Sun, Weijie Zhang, Runxiang Yu et al., https://doi.org/10.1109/access.2021.3076530, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.