AI-Powered Digital Twins for Robotic Control in 5G-Enabled Industrial Automation

Abstract

This paper introduces a novel approach to AI-powered digital-twins-assisted robotic control in automated warehouses, integrating the kinetic models of robots with real-time synchronization of digital-twins. The proposed framework utilizes Ultra-Reliable Low-Latency Communication (URLLC) over 5G networks to enable seamless interaction between the physical robots and AI-driven models in the cyber twin. We formulate an optimization problem aimed at minimizing energy consumption during digital-twins-driven robotic operations, thereby enhancing both operational efficiency and energy efficiency. A Deep Reinforcement Learning (DRL)-based approach is developed for the adaptive learning of the AI models in the cyber twin, facilitating autonomous simulation and real-time decision-making for efficient robotic control. Additionally, we propose a game-theory-based resource allocation strategy to optimize the distribution of computational resources for continuous and adaptive learning within AI models. Numerical results demonstrate that the proposed game-based resource allocation scheme achieves Nash equilibrium, significantly improving performance in terms of energy consumption and resource utilization compared to the state-of-the-art DRL-based resource allocation scheme.