Motion planning is a crucial aspect of robot autonomy as it involves identifying a feasible motion path to a destination while taking into consideration various constraints, such as input, safety, and performance constraints, without violating either system or environment boundaries. This becomes particularly challenging when multiple robots run without communication, which compromises their real-time efficiency, safety, and performance. In this paper, we present a learning-based potential field algorithm that incorporates deep reinforcement learning into an artificial potential field (APF). Specifically, we introduce an observation embedding mechanism that pre-processes dynamic information about the environment and develop a soft wall-following rule to improve trajectory smoothness. Our method, while belonging to reactive planning, implicitly encodes environmental properties. Additionally, our approach can scale up to any number of robots and has demonstrated superior performance compared to APF and RL through numerical simulations. Finally, experiments are conducted to highlight the effectiveness of our proposed method.

翻译：运动规划是机器人自主性的关键方面，涉及在考虑各种约束（如输入、安全和性能约束）且不违反系统或环境边界的情况下，确定通向目的地的可行运动路径。当多机器人在无通信条件下运行时，这一问题变得尤为具有挑战性，这会损害其实时效率、安全性和性能。本文提出一种基于学习的势场算法，该算法将深度强化学习融入人工势场（APF）中。具体而言，我们引入一种观测嵌入机制，用于预处理环境的动态信息，并开发了一种软壁跟随规则以提高轨迹平滑度。尽管属于反应式规划范畴，我们的方法隐式地编码了环境属性。此外，我们的方法可扩展至任意数量的机器人，并通过数值模拟展示了相比于APF和强化学习（RL）的优越性能。最后，通过实验验证了所提方法的有效性。