Decentralized Triangulation Formation without Communication: A Vision Transformer Based Learning Approach

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 LIDAR sensor data as input, and the control policy is learned from demonstration provided by an expert controller in a decentralized way. While training 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 varying sizes using the learned control policy.