移動ロボットの動作計画手法レビュー：古典的ワークフローから強化学習ベースのアーキテクチャまで
A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures
移動ロボットの動作計画について、古典的な階層型手法の各モジュールと強化学習ベース手法（地図不要型やマルチロボット協調を含む）を体系的にレビューし、課題と今後の研究方向を整理した。
著者: Lu Dong, Zichen He, Chunwei Song, Changyin Sun
分類: cs.RO
原文アブストラクト
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.
