  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.
