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Motion planning is challenging for multiple robots in cluttered environmentswithout communication, especially in view of real-time efficiency, motionsafety, distributed computation, and trajectory optimality, etc. In this paper,a reinforced potential field method is developed for distributed multi-robotmotion planning, which is a synthesized design of reinforcement learning andartificial potential fields. An observation embedding with a self-attentionmechanism is presented to model the robot-robot and robot-environmentinteractions. A soft wall-following rule is developed to improve the trajectorysmoothness. Our method belongs to reactive planning, but environment propertiesare implicitly encoded. The total amount of robots in our method can be scaledup to any number. The performance improvement over a vanilla APF and RL methodhas been demonstrated via numerical simulations. Experiments are also performedusing quadrotors to further illustrate the competence of our method.

Reinforced Potential Field for Multi-Robot Motion Planning in Cluttered Environments