Projects

RUGGED addresses a critical gap in autonomous ground robotics: reliable operation in harsh, unstructured outdoor environments such as agriculture, forestry, and wildfire scenarios. While perception, localization, and navigation have advanced rapidly in controlled settings, little is known about how robotic systems and sensors degrade under dust, smoke, debris, extreme vibrations, and high temperatures. RUGGED tackles this problem head-on by jointly addressing hardware robustness and software resilience.The project follows a strongly experimental, multidisciplinary approach combining electrical and mechanical engineering with robotics and AI. It begins with a systematic assessment of how environmental stressors affect sensors, electronics, and perception pipelines, using both laboratory tests and real-world field trials with forestry machinery. Based on this evidence, RUGGED develops mitigation strategies at two levels: hardware solutions, such as optimized sensor placement, vibration damping, and thermal management; and software solutions, including robust multisensory perception, localization, and mapping.On the software side, the project advances real-time sensor fusion using complementary modalities (LiDAR, RADAR, depth, multispectral and thermal cameras), metric-semantic mapping, traversability analysis, and multimodal localization. These methods are developed and validated using noise-rich virtual environments and real datasets, within a ROS-based framework. The project concludes with large-scale field validation in progressively challenging scenarios and the release of open multisensory datasets. RUGGED ultimately delivers practical guidelines, validated technologies, and data resources to enable dependable semi-autonomous robots in extreme outdoor operations.

ACHILLES delivers a modular framework for developing ML-based systems that are Lighter, Clearer, and Safer, inherently aligned with a broad spectrum of compliance requirements. ACHILLES aims to create an efficient, compliant, and ethical AI ecosystem, addressing challenges related to privacy, security, fairness, and transparency.

The project proposes an iterative development cycle inspired by clinical trials, consisting of four modules focused on human-centric, data-centric, model-centric, and deployment-centric strategies. This approach seeks to enhance the performance and reliability of AI systems while ensuring compliance with legal and ethical standards. A key innovation is the development of a machine learning-driven Integrated Development Environment (IDE), which will streamline the integration between modules and promote the creation of responsible AI solutions.

Involving 16 partners from 10 countries, ACHILLES seeks to strengthen the European AI ecosystem, validating its applications in real use cases such as healthcare, identity verification, content creation, and pharmaceuticals.

More than 2 million new cases of colorectal cancer are diagnosed every year around the globe. The entire global population, living anywhere on earth, above the age of 45 50 can be potentially affected by this challenge. Colorectal cancer remained the second leading cause of cancer-related deaths, accounting for 12 to 14% of all cancers recorded in Europe in 2012, and contributes U$14 billion to annual healthcare costs in the United States alone. In Asia, incidence rates range from 49.3 in Japan, 24.7 in South Korea, and 35.1 in Singapore, and they are equally high in many African and South American countries, turning this into a global challenge. Current scientific breakthroughs enable the development of non-invasive, high-quality imaging, energy-efficient, and miniaturized electronic devices that can travel inside the gastrointestinal tract using natural body cavities. Applying this technology to the screening of large groups, once they reach the age of 45 50, can significantly lower the number of new cases in an advanced stage of progression that are diagnosed every year. 

Conventional computing, implementing for instance Convolutional Neural Networks, transfer learning, and ensemble learning, struggle with the computational load of analysing billions of hours of track images used. By using quantum computing, it could be possible to solve some complex optimisation and pattern recognition tasks more efficiently than classical computers. Quantum algorithms such as Quantum Neural Networks and Quantum Support Vector Machines could enhance the efficiency and accuracy of image analysis, and quantum features space could provide more nuanced insights from the data, potentially improving detection rates.

EU-TRACE (EUropean TRansformers ACcelerated Efficiency), supported by the LIFE programme of the European Commission, designed to cut electricity waste by replacing old, inefficient power transformers much faster than usual.

EU-TRACE works across seven EU countries (Germany, Spain, Portugal, Greece, the Netherlands, Italy, and Belgium) to:

• - Develop new policies, tools, and incentives to speed up transformer replacement.
• - Help energy companies and private businesses adopt high-efficiency transformers.
• - Monitor and predict the impact of these policies on energy savings and greenhouse gas reduction.
• - Promote recycling and sustainable materials in transformer production.
• - Engage with stakeholders to ensure the solutions are practical and effective.

By doing this, the project aims to save an extra 2.1 TWh of electricity every year over the next 20 years, supporting Europe’s climate and energy efficiency goals.

The NewGenVIP project aims to develop and optimize, from an ecodesign perspective, a new range of industrial mixers for Vacuum Infusion Processes (VIP), integrating real-time monitoring and decision support through the application of artificial intelligence powered by historical data and new instrumentation developed within the project. Among the planned innovations are the ability to operate with multiple infusion channels simultaneously, an active degassing system, an IoT interface, and tools that enable comprehensive tracking of operating conditions. The goal is to reduce the occurrence of failures, improve efficiency, safety, and sustainability in industrial processes within the composites sector, increase the quality of final products, and lower operational costs.