If you are an operator dealing with high maintenance costs and dangerous manual inspections — this project developed a multi-domain robotic fleet that can lower the levelized cost of electricity (LCOE) by 2.5%. It reduces worker risk exposure by 90% and cuts downtime by 60%.
Automated Robotic Fleet for Offshore Wind Farm Inspection and Maintenance
Imagine a team of robot drones and underwater subs that work together like a coordinated swarm to check on wind turbines. Instead of sending divers and climbers into dangerous seas, these robots do the dirty work of cleaning and inspecting both above and below the water. It's like having a remote-controlled security and maintenance crew that never gets tired or puts lives at risk.
What needed solving
Offshore wind maintenance is expensive, dangerous for humans, and causes significant downtime. Current methods rely on manual inspections that increase the levelized cost of electricity.
What was built
A coordinated fleet of Uncrewed Fleet Carriers, surface vehicles (USV), underwater ROVs, and aerial drones (UAS/UAV) integrated with AI and Digital Twins.
Who needs this
Who can put this to work
If you are a service provider dealing with the inefficiency of manned ROV operations — this project developed an unmanned robotic solution for underwater intervention. This can increase operational efficiency by 40% and reduce the human monitoring burden by 80%.
If you are a firm dealing with the high carbon footprint of vessel-based maintenance — this project developed an automated orchestration of drones and surface vehicles. This approach has the potential to reduce eCO2 emissions of operations by up to 15M tonnes.
Quick answers
How much can this solution save in operational costs?
Based on available project data, the solution enables cost savings of 2,400€/MW/year.
Is this technology ready for industrial scale?
Yes, the project involves large scale pilots for monitoring, inspection, cleaning, and maintenance in real offshore wind farms.
What are the IP or licensing options for the AI and Digital Twin components?
Based on available project data, specific IP or licensing terms are not provided, but the project develops AI-based features for perception and Digital Twin solutions for data analysis.
How does this integrate with existing human workflows?
The system optimizes human-robot and robot-robot collaboration to reduce the human burden for monitoring by 80%.
What is the timeline for deployment?
The project period runs from 2024-12-01 to 2028-11-30.
Who built it
The consortium is heavily industry-driven with 11 industrial partners (65% ratio), including 4 SMEs, which suggests a strong focus on commercial viability. With 17 partners across 6 countries (PT, ES, FR, IE, IT, UK), the project combines academic research from 3 universities and 3 research centers with practical industrial application.
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