AUTHOR=Jiang Chao , Huang Xinchi , Guo Yi 

TITLE=End-to-end decentralized formation control using a graph neural network-based learning method

JOURNAL=Frontiers in Robotics and AI

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2023.1285412

DOI=10.3389/frobt.2023.1285412

ISSN=2296-9144

ABSTRACT=Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline of design, and the separation of perception and controls may cause processing latency and compounding errors that affect control performance. End-to-end learning overcomes such limitation by learning directly from onboard sensing data, and outputs control command to robots. Challenges exist in end-to-end learning for multi-robot cooperative control and previous results are not scalable. We propose in this paper a novel decentralized cooperative control method for multi-robot formation using deep neural networks, in which inter-robot communication is modeled by a graph neural network (GNN). Our method takes the LIDAR sensor data as input, and the control policy is learned from demonstrations that are provided by an expert controller for decentralized formation control. While trained with a fixed number of robots, the learned control policy is scalable. Evaluation in a robot simulator demonstrates the triangulation formation behavior of multi-robot teams with different sizes using the learned control policy.