Multi-Robot Coverage Path Planning based on Deep Reinforcement Learning

Abstract

The multi-robot coverage path planning (CPP) is the design of optimal motion sequence of robots, which can make robots execute the task covering all positions of the work area except the obstacles. In this article, the communication capability of the multi-robot system is applied, and a multi-robot CPP mechanism is proposed to control the robots to perform CPP tasks in an unknown environment. In this mechanism, an algorithm based on deep reinforcement learning is proposed, which can generate the next action for robots in real-time according to the current state of the robots. In addition, a real-time obstacle avoidance scheme for multi-robot is proposed based on the information interaction capability of multi-robot. Experiment results show that the method can plan the optimal path for multi-robot to complete the covering task in an unknown environment. Moreover, compared with other reinforcement learning methods, the algorithm proposed can efficiently learning with fast convergence speed and good stability.