AR/VR Digital Twin for simulation and data collection of robotic environments

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Marcelo Petry (Author) FEUP; Martins, JG (Author) Other; Nutonen, K (Author) Other; Costa, P (Author) Other; Kuts, V (Author) Other; Otto, T (Author) Other; Armando Jorge Sousa (Author) FEUP

Conference proceedings International

Title: 2025 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Pages: 198-203

2025 International Conference on Autonomous Robot Systems and Competitions-ICARSC-Annual

Funchal, PORTUGAL, APR 02-03, 2025

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-018-NVS

DOI: 10.1109/icarsc65809.2025.10970158

Abstract (EN): Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6