Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications

TL;DR

This paper develops a Digital Twin framework for AGVs using integrated sensing and communications, employing reinforcement learning to optimize control and resource allocation in real-time, validated through mmWave simulations.

Contribution

It introduces a novel Digital Twin framework that combines sensing, communication, and reinforcement learning for AGV control in networked environments.

Findings

Enhanced control precision demonstrated in simulations

Improved communication efficiency through resource allocation

Effective RL-based adaptation to positional uncertainty

Abstract

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.