Remote handling operation for IFMIF-DONES supported by time-sensitive networking
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Vázquez Rodríguez, Víctor; Valenzuela Segura, Elio; Shepstone, Ricardo; Megías Núñez, Carlos; Miccichè, Gioacchino; Ros Vidal, Eduardo; Barranco Expósito, FranciscoMateria
IFMIF-DONES time-sensitive networking remote handling artificial intelligence
Date
2026-03-20Referencia bibliográfica
Víctor Vázquez et al 2026 Nucl. Fusion 66 046029
Sponsorship
European Union via the Euratom Research and Training Programme (Grant 101052200—EUROfusion); MICIU/AEI/10.13039/501100011033 and ERDF/EU (Grant PID2022-141466OB-I00); MICIU/AEI/10.13039/501100011033, FPU PhD Fellowship (Grant FPU20/05842); MICIU/AEI/10.13039/501100011033, FPU PhD Fellowship (Grant FPU20/01857); Universidad de Granada, Programa de Contratos-Puente (Grant CP-2025-37); Funding for open access charge: Universidad de GranadaAbstract
Experimental fusion research facilities, such as the International Fusion Materials Irradiation Facility-DEMO Oriented Neutron Source (IFMIF-DONES), require advanced remote handling (RH) systems to perform maintenance and inspection tasks in a safe and reliable manner, due to their intrinsic high-radiation nature. The mixed-criticality requirements of the data streams used in these systems force the deployment of separate networks and communication technologies. Commonly, it includes fieldbuses for traffic control, standard Ethernet for video and general-purpose traffic, and dedicated networks for the most critical safety-related signals. This fragmentation leads to complex and costly deployments and also prevents the application of models for predictive maintenance or advanced monitoring. The time-sensitive networking (TSN) technology stack aims to provide deterministic behaviour for data transmission over standard Ethernet, allowing for convergence on a single network and ensuring bounded latencies for critical traffic. In this work, we propose a design and validate the TSN-based communication architecture for the RH system of IFMIF-DONES. The design ensures bounded delivery times for safety-critical interlock signals, achieving a worst-case delay under 30 us even under high network load. The proposed network is also validated in a real robotic teleoperation task, where artificial intelligence is applied for object detection and tracking, using mixed-criticality video streams. Our results show that TSN traffic shapers are essential in providing the necessary latency and bandwidth guarantees for such teleoperation tasks, enabling network convergence in this kind of deployments.





