Closing the Digital twin loop Through human interaction using Extended Reality: a case study on laser cleaning

Industry 5.0 emphasises the integration of human intelligence with advanced automation, promoting collaboration between humans and machines in manufacturing environments. This paper presents an Extended Reality (XR)-driven Digital Twin (DT) framework for intelligent robotic laser cleaning, designed to enhance human-robot collaboration through immersive and intuitive interfaces. The system integrates a YOLOv11x-OBB-based defect-detection module, adaptive path-planning algorithms, and gesture-based robot control within Mixed Reality (MR) and Virtual Reality (VR) environments. The DT, implemented in Unity and synchronised with a UR10e collaborative robot via ROS2 (Robot Operating System 2)-RTDE (Real-Time Data Exchange) communication, enables real-time monitoring, trajectory validation, and bidirectional data exchange between physical and virtual systems. Experimental results demonstrate accurate defect localisation ([email protected] = 0.995, [email protected]:0.95 = 0.977), responsive hand-gesture interaction (438 ms average latency), and stable DT synchronisation. Furthermore, the proposed defect-targeted path-planning strategy reduced laser-cleaning time by 86% compared with full-surface cleaning, highlighting its potential for industrial productivity and sustainability. The results confirm that combining XR, DT, and AI-based perception establishes an effective human-centric framework for intelligent robotic surface processing, supporting future deployment in Industry 5.0 manufacturing systems.