Optical Integrated Sensing and Communication for Cooperative Mobile Robotics Design and Experiments

TL;DR

This paper introduces a novel optical sensing and communication scheme for cooperative mobile robots, enabling real-time control and improved formation stability using onboard cameras without relying on radio communication.

Contribution

The paper presents an innovative optical ISAC scheme tailored for mobile robots, integrating camera sensing with screen-camera communication for real-time control in radio-deprived environments.

Findings

OISAC improves formation accuracy and stability.

Real-world experiments validate system robustness.

Outperforms EKF-based benchmark in various motions.

Abstract

Integrated Sensing and Communication (ISAC) is an emerging technology that integrates wireless sensing and communication into a single system, transforming many applications, including cooperative mobile robotics. However, in scenarios where radio communications are unavailable, alternative approaches are needed. In this paper, we propose a new optical ISAC (OISAC) scheme for cooperative mobile robots by integrating camera sensing and screen-camera communication (SCC). Unlike previous throughput-oriented SCC designs that work with stationary SCC links, our OISAC scheme is designed for real-time control of mobile robots. It addresses new problems such as image blur and long image display delay. As a case study, we consider the leader-follower formation control problem, an essential part of cooperative mobile robotics. The proposed OISAC scheme enables the follower robot to simultaneously acquire the information shared by the leader and sense the relative pose to the leader using only RGB images captured by its onboard camera. We then design a new control law that can leverage all the information acquired by the camera to achieve stable and accurate formations. We design and conduct real-world experiments involving uniform and nonuniform motions to evaluate the proposed system and demonstrate the advantages of applying OISAC over a benchmark approach that uses extended Kalman filtering (EKF) to estimate the leader's states. Our results show that the proposed OISAC-augmented leader-follower formation system achieves better performance in terms of accuracy, stability, and robustness.