IJMO 2026 Vol.16(1): 6-10
DOI: 10.7763/IJMO.2026.V16.876
Real-Time Interaction and Monitoring of Collaborative Robots via AR/VR and Digital Twin in IIoT Frameworks
Dragos-Alexandru Cazacu 1,*, Matei Drăguțu2, Mihail Hanga3, Carmen-Cristiana Cazacu3, and Florina Chiscop3
1. PTC Eastern Europe SRL, Romania and Robots and Production Systems Department, The Faculty of Industrial Engineering and Robotics,
National University of Science and Technology Politehnica, Bucharest, Romania
2. Machine of Construction Technology Department, The Faculty of Industrial Engineering and Robotics, National University of Science and
Technology Politehnica, Bucharest, Romania
3. Robots and Production Systems Department, The Faculty of Industrial Engineering and Robotics, National University of Science and Technology Politehnica, Bucharest, Romania
Email: acazacu@ptc.com (D.A.C.); matei.dragutu@stud.fiir.upb.ro (M.D.); mihail.hanga@stud.fiir.upb.ro (M.H.);
carmen.cazacu@upb.ro (C.C.C.); florina.chiscop@upb.ro (F.C.)
*Corresponding author
Manuscript received November 5, 2025; accepted December 16, 2025; published February 3, 2026.
Abstract—The paper proposes a Digital Twin framework for a six-axis collaborative robotic arm, integrating Augmented Reality (AR)/Virtual Reality (VR), the Industrial Internet of Things (IIoT), and CAD modeling for real-time monitoring and interaction. The architecture comprises a physical subsystem an xArm Lite 6 controlled via UFactory’s SDK and a Python pipeline that collects joint angles and publishes them to the ThingWorx platform and a virtual subsystem built in Unity, which retrieves this data through an API and updates the 3D replica every frame. To preserve kinematic consistency, the CAD model is joint-hierarchized, and transitions are smoothed via interpolation (Mathf.LerpAngle). Experimental evaluations indicate robust synchronization between the robot and its digital twin, with POST latencies of 191 ± 16 ms, GET latencies of 175 ± 15 ms, and end-to-end delays of 1015 ± 85 ms, suitable for operational visualization and decision support. Results show that combining DT–AR/VR–IIoT enhances system transparency, facilitates analysis and training, and lays the groundwork for scaling to multi-robot scenarios, teleoperation, and human–robot collaboration. Identified limitations include latency optimization, increasing the sampling rate, and expanding the set of synchronized variables. Compared to prior AR/VR–DT–IIoT frameworks, the core contribution lies in designing and validating a portable, interoperable real-time synchronization loop, built on accessible technologies, that achieves sub-second end-to-end responsiveness in a validated physical robotic setup, thereby addressing gaps in kinematic fidelity and operator-in-the-loop interaction.
Keywords—augmented reality, virtual reality, Industrial Internet of Things (IIoT), digital twin, Computer Aided Detection (CAD)
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Cite: Dragos-Alexandru Cazacu, Matei Drăguțu, Mihail Hanga, Carmen-Cristiana Cazacu, and Florina Chiscop, "Real-Time Interaction and Monitoring of Collaborative Robots via AR/VR and Digital Twin in IIoT Frameworks," International Journal of Modeling and Optimization, vol. 16, no. 1, pp. 6-10, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
