The ARIES pilot addresses the pressing challenge of modernising inspection practices within Extra High Voltage Centres
(EHVCs), where current time-based maintenance methods rely heavily on manual, scheduled thermographic inspections
that are infrequent, resource-intensive, and prone to delays due to logistical and safety constraints. These conventional
approaches limit the ability to detect emerging electrical faults in their early stages, increasing the risk of equipment failure
and costly outages. By introducing a robotic system equipped with advanced sensing capabilities and AI-based defect
detection, the project enables condition-based maintenance that improves fault anticipation, optimises resource allocation,
and significantly reduces the need for routine site visits. Robotics offers the added benefit of consistent, high-frequency
data acquisition across large and hazardous environments, without exposing personnel to unnecessary risk. To address
the limitations of traditional teleoperation, such as poor depth perception and operator disorientation, the ARIES solution
incorporates a VR-based interface with shared-control navigation and augmented reality overlays, enhancing situational
awareness, reducing cognitive load, and enabling precise, remote interaction with complex infrastructure.
The ARIES pilot aims to demonstrate a next-generation robotic inspection system for Extra High Voltage Centres (EHVCs),
enabling remote, condition-based maintenance through immersive VR teleoperation and real-time AR overlays. By
integrating 360° video streaming, thermal imaging, and AI-driven defect detection, the system allows operators to remotely
inspect critical infrastructure with greater safety, precision, and efficiency. The pilot will be deployed and validated in a live
EHVC operated by IPTO, a complex and densely equipped environment where traditional inspections are logistically
demanding and safety-critical—providing a rigorous, real-world setting to assess system performance, usability, and impact
on operational workflows.
● VR-based teleoperation using live 360° video for immersiveremote control
● Shared-control navigation combining operator input with real-time terrain safety filtering
● AR overlays displaying component labels, condition data, and metadata in the VR view
● Manual and gaze-assisted thermal camera targeting for precise inspections
● AI-based defect detection on thermal images to identify faults automatically
● Automated inspection logging and report generation with structured output
● Integration with ENORASI platform and JARVIS visualization module.
This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement nº 101135708. The dissemination of results herein reflects only the author’s view, and the European Commission is not responsible for any use that may be made of the information it contains.
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