Motion planning is challenging for multiple robots in cluttered environments without communication, especially in view of real-time efficiency, motion safety, distributed computation, and trajectory optimality, etc. In this paper, a reinforced potential field method is developed for distributed multi-robot motion planning, which is a synthesized design of reinforcement learning and artificial potential fields. An observation embedding with a self-attention mechanism is presented to model the robot-robot and robot-environment interactions. A soft wall-following rule is developed to improve the trajectory smoothness. Our method belongs to reactive planning, but environment properties are implicitly encoded. The total amount of robots in our method can be scaled up to any number. The performance improvement over a vanilla APF and RL method has been demonstrated via numerical simulations. Experiments are also performed using quadrotors to further illustrate the competence of our method.

翻译：运动规划对于多机器人在无通信的杂乱环境中具有挑战性，尤其是在实时效率、运动安全、分布式计算和轨迹最优性等方面。本文提出了一种增强势场法用于分布式多机器人运动规划，该方法融合了强化学习与人工势场技术的综合设计。通过引入自注意力机制的观测嵌入方法，对机器人与机器人之间以及机器人与环境之间的交互进行建模。开发了软避障规则以提升轨迹平滑度。该方法属于反应式规划范畴，但隐式编码了环境特性。所提方法中机器人总数可扩展至任意数量。通过数值仿真验证了该方法相较于传统APF和RL方法的性能提升。进一步采用四旋翼飞行器进行实验，证明了该方法的能力。