Established in 2015, this Award is aimed at university students studying the sciences and (for the first time this year) economics, in order to draw on their talent and ideas. The competition asked the candidates, who were divided into two categories (Students/Graduates and Ph.D. Students/ Ph.D. Graduates), to develop an innovative project in one of a number of specific areas of research which have been identified as having potential for Leonardo’s future business: Virtual Reality, Blockchain, Cognitive Systems, Circular Economy.
University students category winners
Leonardo Innovation Award: winners of the Students/Graduates category
Virtual Reality - Glove Controller.
The project involves a remote control flight system, in the form of a sensory glove, which can manage complex processes such as control of a drone. The project comprises a sensory glove and a drone managed by an ‘Arduino’ single-board flight controller, as well as a series of additional sensors. The proposed solution allows for the management of more complex tasks than those which are typically possible with conventional methods such as joysticks. The project is aligned with the needs of Leonardo in the management of remote control platforms.
Blockchain - Blockchain for aeronautical maintenance.
The aim of the project is to apply blockchain technology in order to store data related to the overhaul and maintenance of aircraft, in a fixed and secure way, in digital and distributed records. A physical maintenance record of an aircraft can be lost or destroyed, and, if it is paper-based, this greatly complicates inspections in terms of cost and time. Physical records are also vulnerable to fraudulent activity, potentially damaging their credibility in the eyes of external stakeholders. This project therefore improves the security, accessibility and integrity of maintenance data.
Circular Economy - SOBRERO.
The current social, economic and environmental landscape requires that industry pivots toward a Green Chemistry approach, aiming to use raw materials of plant origin. This is what is meant by moving from a linear economy model to a circular economy model. The SOBRERO project focuses on the idea of synthesising bio-based binders to be used in solid propellants, which are currently used for propulsion in the aerospace field. Replacing the petrochemical-based polyurethane binder with a similar binder made from renewable sources would bring numerous advantages in terms of environmental sustainability. In addition, the synthesis of bio-based products as an alternative to the naturally limited synthesis of raw fossil materials supports the long-term sustainability of propulsion solutions.
Leonardo Innovation Award: winners of the Ph.D. Students/ Ph.D. Graduates category
Ph.D. Students/ Ph.D. Graduates category
Blockchain - How Blockchain can revolutionise air transport.
The idea behind the project was to create a platform based on blockchain technology that allows airport operators to quickly and reliably verify the identity of passengers, resulting in economic savings for the community at large. The solution will also help address the problem of passengers who manage to evade controls and enter foreign countries without a visa or with false documents.
Cognitive Systems - Use of deep learning to support airport security checks.
A deep learning approach could help identify possible threats while screening baggage at airports, using X-ray instrumentation. The solution involves software architecture made up of several artificial neural networks, which work together to create a more precise ability to identify dangerous objects inside luggage.
Cognitive Systems - An artificial intelligence tool for the migration analysis and projections.
The project explores the predictive potential offered by deep learning, applying mechanisms from the field to address the challenge of forecasting in the social sciences, and in particular the forecasting of international migration flows. An artificial neural network model is the basis for modelling the probable direction and timing of migration and identifying the most probable countries of origin and arrival in the medium-to-long term.