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.

翻译：运动规划对于实现移动机器人的自主操作至关重要。随着机器人应用设想方案的复杂性和复杂性的提高,古典等级运动规划者的规划能力受到挑战。近年来,随着智能计算技术的发展,基于深强化学习(DRL)的运动规划算法由于其优点,如不依赖先前的、无模型的和统一的全球和地方规划模式等优势,逐渐成为一个研究热点。在本文件中,我们系统地审查了各种运动规划方法。首先,我们总结了典型运动规划结构每个子模块的代表和尖端算法,并分析了其绩效限制。在其余部分,我们侧重于审查基于RL的运动规划方法,包括RL优化运动规划器、不设地图的终端到终端方法,将遥感和决策结合起来,以及多机器人合作规划方法。在最后一节,我们详细分析这些方法所面临的紧迫挑战,审查这些问题的一些最新算法,并为未来研究提出建议。