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Abstract：
Path planning is an crucial research area in robotics. Compared to other path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm possesses both search and random sampling properties, and thus has more potential to generate high-quality paths that can balance the global optimum and local optimum. This paper reviews the research on RRT-based improved algorithms from 2021 to 2023, including theoretical improvements and application implementations. At the theoretical level, branching strategy improvement, sampling strategy improvement, post-processing improvement, and model-driven RRT are highlighted, at the application level, application scenarios of RRT under welding robots, assembly robots, search and rescue robots, surgical robots, free-floating space robots, and inspection robots are detailed, and finally, many challenges faced by RRT at both the theoretical and application levels are summarized. This review suggests that although RRT-based improved algorithms has advantages in large-scale scenarios, real-time performance, and uncertain environments, and some strategies that are difficult to be quantitatively described can be designed based on model-driven RRT, RRT-based improved algorithms still suffer from the problems of difficult to design the hyper-parameters and weak generalization, and in the practical application level, the reliability and accuracy of the hardware such as controllers, actuators, sensors, communication, power supply and data acquisition efficiency all pose challenges to the long-term stability of RRT in large-scale unstructured scenarios. As a part of autonomous robots, the upper limit of RRT path planning performance also depends on the robot localization and scene modeling performance, and there are still architectural and strategic choices in multi-robot collaboration, in addition to the ethics and morality that has to be faced. To address the above issues, I believe that multi-type robot collaboration, human-robot collaboration, real-time path planning, self-tuning of hyper-parameters, task- or application-scene oriented algorithms and hardware design, and path planning in highly dynamic environments are future trends.
摘要：
路径规划是机器人学的一个重要研究领域。与其他路径规划算法相比，快速探索随机树(RRT)算法同时具有搜索和随机抽样特性，因此具有更多的潜力来生成可以平衡全局最优和局部最优的高质量路径。本文回顾了2021-2023年基于RRT的改进算法的研究,包括理论改进和应用实现。在理论层面,分支战略改进，抽样策略的改进，后处理改进，突出显示了模型驱动的RRT，在应用层面,RRT在焊接机器人下的应用场景，装配机器人,搜索和救援机器人，手术机器人,自由漂浮的太空机器人，和检测机器人是详细的，最后，总结了RRT在理论和应用层面面临的诸多挑战。这篇综述表明，尽管基于RRT的改进算法在大规模场景中具有优势，实时性能，和不确定的环境，一些难以定量描述的策略可以基于模型驱动的RRT来设计，基于RRT的改进算法仍然存在难以设计超参数和泛化能力弱的问题,在实际应用层面,控制器等硬件的可靠性和准确性，执行器,传感器,通信,电源和数据采集效率都对大规模非结构化场景下RRT的长期稳定性提出了挑战。作为自主机器人的一部分，RRT路径规划性能的上限还取决于机器人的定位和场景建模性能,在多机器人协作中仍然存在架构和战略选择，除了必须面对的伦理和道德。为了解决上述问题，我相信多类型机器人协作，人机协作,实时路径规划，超参数的自整定，面向任务或应用场景的算法和硬件设计，高度动态环境中的路径规划是未来的发展趋势。
