We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning algorithm, called \emph{RRT*former}, which integrates the standard RRT* algorithm with a Transformer network in a novel way. Specifically, the Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.

翻译：本研究探讨了机器人在复杂动态环境中的基于采样的最优路径规划问题。现有的大多数基于采样的算法忽略了环境信息或先前采样点的信息。然而，这些信息具有高度参考价值，利用它们可以为下一状态的采样提供更好的启发式策略。本文提出了一种新颖的基于采样的规划算法，称为\\emph{RRT*former}，该算法以创新方式将标准RRT*算法与Transformer网络相结合。具体而言，Transformer用于从环境中提取特征，并利用先前采样点的信息以更好地指导采样过程。我们的大量实验表明，与现有的基于采样的方法（如RRT*、Neural RRT*及其变体）相比，我们的算法在路径最优性和采样效率方面均取得了显著提升。实现代码发布于https://github.com/fengmingyang666/RRTformer。