{"id":191491,"date":"2026-01-07T16:34:12","date_gmt":"2026-01-07T15:34:12","guid":{"rendered":"https:\/\/eurecat.org\/portfolio-items\/amelia-hub\/"},"modified":"2026-02-11T13:25:10","modified_gmt":"2026-02-11T12:25:10","slug":"amelia-hub","status":"publish","type":"avada_portfolio","link":"https:\/\/eurecat.org\/en\/portfolio-items\/amelia-hub\/","title":{"rendered":"AMELIA-HUB"},"content":{"rendered":"

\"AMELIA-HUB<\/p>\n<\/div>

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Autonomous system for internal blade inspection aimed at predictive maintenance in wind turbine blades.<\/p>\n

The project develops an autonomous system for the internal inspection of wind turbine blades using a pendular drone specifically designed to operate in confined environments with complex internal geometries<\/strong>. The system integrates advanced sensing technologies\u2014such as LiDARs (Light Detection and Ranging), optical and thermal cameras, and ultrasound sensors\u2014combined with artificial intelligence techniques to accurately and automatically detect structural defects.<\/p>\n

In addition, the project incorporates GPS-denied autonomous navigation based on SLAM techniques (Simultaneous Localization and Mapping), advanced trajectory control and real-time data transmission, all powered through an umbilical system that ensures continuous operational autonomy.<\/p>\n

The Robotics and Automation Unit of Eurecat<\/strong><\/a> contributes to the project by developing the internal localization system, the control and navigation software, and a high-fidelity simulator to validate the localization and control systems. It is also responsible for the supervision and teleoperation system with shared autonomy and provides support in the development of the hardware platform.<\/p>\n

The consortium, coordinated by Amelia Lab, brings together expertise in robotics, artificial intelligence, computer vision, wind energy and predictive maintenance, with the aim of setting a new standard in internal blade inspection and in the sustainability of the wind energy sector.<\/p>\n<\/div>

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General details<\/h3><\/span>
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Project<\/strong><\/p>\n

Innovation in Inspection for the Predictive Maintenance of Wind Turbine Blades Interiors<\/p>\n

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Project reference<\/strong><\/p>\n

CPP2024-011710<\/p>\n<\/div>

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Programme and call\u00a0<\/strong><\/p>\n

Project funded by the Spanish State Research Agency (AEI) of the Ministry of Science, Innovation and Universities under the 2024 \u201cColaboraci\u00f3n P\u00fablico-Privada\u201d Projects call.<\/p>\n

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Related SDGs<\/strong><\/p>\n

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SDG 7 \u2013 Renewable Energy:<\/strong> Contributes to improving the efficiency and availability of wind turbines, enabling a more stable supply of renewable energy.<\/p>\n

SDG 9 \u2013 Industry, Innovation and Infrastructure:<\/strong> Develops advanced robotic technology, artificial intelligence, and predictive maintenance systems for critical energy infrastructures.<\/p>\n

SDG 11 \u2013 Sustainable Cities and Communities:<\/strong> Improves the reliability of urban energy networks by reducing outages and optimizing maintenance.<\/p>\n

SDG 13 \u2013 Climate Action:<\/strong> Increases the availability of wind energy and minimizes unplanned downtime, strengthening the transition towards clean energy.<\/p>\n<\/div>

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