基于综合传感与通信的自主制导车辆数字孪生系统

Van-Phuc Bui, Pedro Maia de Sant Ana, Soheil Gherekhloo, Shashi Raj Pandey, Petar Popovski
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引用次数: 0

摘要

本文介绍了一种数字孪生(DT)框架,用于在网络控制系统(NCS)中远程控制自主导航车(AGV)。AGV 采用集成传感与通信(ISAC)进行监控。为了满足实时性要求,DT 计算控制信号并动态分配传感和通信资源。我们提出了一种强化学习(RL)算法,用于学习和提供合适的行动,同时调整 AGV 位置的不确定性。我们提出了可实现通信速率和克拉默-拉奥约束(CRB)的闭式表达式,以确定所需的正交频分复用(OFDM)子载波数量,从而满足传感和通信的需要。通过毫米波(mmWave)仿真验证了所提出的算法,证明该算法在控制精度和通信效率方面都有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital Twin for Autonomous Guided Vehicles based on Integrated Sensing and Communications
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.
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