Collision-aware Multi-robot Motion Coordination Deep-RL with Dynamic Priority Strategy

The motion coordination of multi-robots is the basis of the multi-robot system (MRS). The motion coordination problem of multi-robot satisfies the Markov property, so deep reinforcement learning (DRL) can be used to solve this problem. There are two limitations in the existing researches which apply DRL to solve this problem: the success rate of training is low and the effect of coordination is poor. We believe that in the training process, the agent should learn the collision constraint relationship from the collision. Therefore, we propose a Partially Tolerant Collision (PTC) collision handling strategy. And we believe that the completion time of motion coordination is related to the remaining distance of robots. Therefore, we propose the Dynamic Priority Strategy (DPS), which sets the priority for the robot based on the remaining distance of robots. This strategy is integrated into the reward setting of DRL. We use Path Checkerboard Diagram (PCD) as the basis for training and simulation. By experimenting with algorithms such as DQN, DDQN, and MLDDQN, the results of our proposed model are better than previous studies.