This paper presents a Digital Twin (DT) framework for the remote control of an Autonomous Guided Vehicle (AGV) within a Network Control System (NCS). The AGV is monitored and controlled using Integrated Sensing and Communications (ISAC). In order to meet the real-time requirements, the DT computes the control signals and dynamically allocates resources for sensing and communication. A Reinforcement Learning (RL) algorithm is derived to learn and provide suitable actions while adjusting for the uncertainty in the AGV's position. We present closed-form expressions for the achievable communication rate and the Cramer-Rao bound (CRB) to determine the required number of Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers, meeting the needs of both sensing and communication. The proposed algorithm is validated through a millimeter-Wave (mmWave) simulation, demonstrating significant improvements in both control precision and communication efficiency.
@article{arxiv.2409.08005,
title = {Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications},
author = {Van-Phuc Bui and Pedro Maia de Sant Ana and Soheil Gherekhloo and Shashi Raj Pandey and Petar Popovski},
journal= {arXiv preprint arXiv:2409.08005},
year = {2024}
}