Recent advances in Rapidly-exploring random tree: A review

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.