Multi-Robot Formation Control using Collective Behavior Model and Reinforcement Learning

Abstract

A multi-robot system has advantages in complex tasks, where formation control is one of the most critical and fundamental tasks. For small-sized, autonomous, and enduring robots, realizing high energy and area efficiency is extremely important. This paper presents a approach that combines swarm intelligence and reinforcement learning to realize accurate and reliable operations. An area-energy-efficient hardware architecture is proposed to perform formation control in a distributed robotic system. The proposed system demonstrates substantially lower cost and power consumption when compared with the state-of-the-art designs.