Ra.Re³ is a research initiative exploring the repurposing of second-life lithium-ion (Li-ion) batteries originally used in Formula E electric vehicles (EVs) for stationary energy storage applications. By extending their operational life, the project contributes to United Nations (UN) Sustainable Development Goals (SDGs), including affordable and clean energy (SDG 7), industry innovation (SDG 9), responsible consumption (SDG 12), and climate action (SDG 13).
The construction industry alone accounts for 37% of global CO2 emissions, with cement production alone accounting for 8%. It is essential to propose a sustainable alternative capable of reducing the impact of this hard-to-abate sector. The Re-Came Stone project proposes an innovative solution: taking waste from stone cutting industry and transforming it into a building component through additive manufacturing (AM)
RECycled Pavilion is a temporary architectural installation realized in the city of Concepción (Chile) as part of the 10th anniversary of the largest free music festival in the country, REC Festival. The project was developed by an international team of students and teachers from the Alta Scuola Politecnica in collaboration with the Universidad del Desarrollo (UDD, Chile) and was realized in March 2025.
Urban areas, due to their high population density, intensive infrastructure, and concentrated economic activities, are responsible for approximately 70% of global greenhouse gas (GHG) emissions and consume around 65% of the world’s total energy. In this context, Renewable Energy Communities (RECs) have emerged as an innovative and decentralize solution capable of reducing the environmental impact of cities by enabling local production, consumption, and sharing of clean energy.
Today, vineyard pruning still relies heavily on skilled human labor. To address this, an increasing number of research initiatives are exploring the transition from traditional manual practices to fully autonomous robotic systems. The VinPRO project contributes to this effort by developing an integrated robotic pruning platform composed of a custom-designed gripper for vineyard environments, an advanced computer vision system to detect branches, a control algorithm to optimize cutting actions, and a virtual reality (VR) tool to model vines and simulate pruning strategies. At its core is a Kinova Gen3 Lite robotic arm, mounted on a mobile rover that serves as the physical backbone of the system.
AI-STABLEPOWER aims at developing methodologies for the analysis of power system stability by means of state-of-the-art artificial intelligence (AI) techniques. Indeed, monitoring stability is a major concern for Transmission System Operators (TSOs), as the ongoing displacement of conventional generators by renewable generation technologies has reduced system inertia, thus affecting system stability.
The AISS4SA project aims to optimize water usage by implementing a modular system that monitors water levels in different paddy field chambers.
The AMBROSE project aims to address the challenge described above by introducing a modern, integrated, multidisciplinary SHM framework. This system will utilize automated monitoring, a combination of direct and indirect sensing methods, and advanced data analysis to detect structural issues early, enabling predictive maintenance and reducing the need for costly, urgent repairs.
The Augmented Reality Vehicle Interface (ARVI) project main goals are: enjoy a unique experience in a rapidly growing startup, learn by doing & get a working methodology, work on cool stuff, having the freedom to build new things from scratch. The project is designed for students that are willing to be out of their comfort zone, ready for complex tasks and to be hands-on.
The project aims to research, develop and implement a highly accurate 24/7 cloud-type classification system (CTCS). This classification system uses two advanced all-sky smart cameras that capture images in both visible and infrared domains, enabling accurate sky coverage classification. Smart all-sky cameras sensors refine and improve data collections and incorporate numerous measures to ensure a consistent data flow and minimize classification errors.
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