Contact Info


Skype: bellonemauro

Mauro Bellone: Roboticist, entrepreneur and innovation technology specialist


I received the M.S. degree in Automation Engineering from the University of Salento, Lecce, Italy, where I received the Ph.D. in Mechanical and Industrial Engineering in 2014.

My interests comprise mobile robotics, autonomous vehicles, energy, computer vision and control systems. My research focuses on the area of advanced sensory perception for mobile robotics and computer vision. In 2009, I had the pleasure to visit the Space Robotics Lab of Tohoku University, Sendai, Japan. In 2013-14, as visiting researcher at University of Almerìa, Spain, I have worked with the Automatic, Electronic and Robotics Research Group TEP-197 studying new advanced navigation techniques for mobile robotics and autonomous driving. From 2015 to 2020, I worked with the applied artificial intelligence research group of Chalmers University of Technology, where I actively contributed on several autonomous driving projects.

Currently, I am studying new algorithms applied to healthcare with Echolight SPA in Italy. In 2021, I was appointed as adjunct professor at Tallinn University of technology supporting the research team in the area of smart transportation systems.

" we are part of a 10 billion people brain "

Guest lecture for BIT

Building robots that perceive reality at least as good as humans do is considered a very complex task, and, with the help of artificial intelligence and sensor fusion, this goals is getting closer and closer.

In consideration of the most recent research developments in the field, this seminar aims to provide an insight and inspiration for graduate students on how the integration of sensory information from several sources can help robotic perception.

PDF presentation available here

Big data and data analysis (in Italiano)

Repository creato per condividere slide e codici affrontati durante il corso IFOA2021 su big data e analisi dati. Il contenuto è reso disponibile e aperto, e copre argomenti che spaziano da aspetti generici riguardo ai big data, il trattamento di big data, e high performance computing.

Le slide descrivono i principali strumenti per il trattamento di big data, sistema di archiviazione basato su file system distribuito Hadoop, negoziatori delle risorse su cluster hadoop, YARN, spark, per raggiungere la principale fonte di utilizzo di big data, l'intelligenza artificiale nei suoi principi chiave.

Tutti i codici dei tutorial e le slide del corso sono disponibili sul repository github.

Dynamic fleet mission planning

This video illustrates our dynamic fleet mission planning method on topological maps, for autonomous underground mining operation. The video shows, at 20x the actual speed, the first hour of a four-hour shift in the mine shown in the figure below.

Vehicles that reach a terminal (either a loading site or an offloading site) can request re-optimization, once they have completed their stationary activity (loading or offloading). At that point, the GA-based optimizer generates a new mission for the vehicle in question, while also (potentially) modifying the missions of the other vehicles. Thus, the planning method must be able to handle the fact that vehicles move during optimization and must make sure to maintain causaility, by only making changes in those parts of the missions that lie beyond the end time of the optimization procedure.

Note also that the video has been speeded up by a factor 20. The actual traversal times are on the order of 7 - 15 minutes. For this rather small mine map, with a single off-loading station, 4-5 vehicles is close to the upper limit of what is reasonable. However, in bigger maps, the fleet mission optimizer can handle larger number of vehicles as well.

More information available on the Adaptive systems research group web page.

Development of a Serious Game to Enhance Assistive Rehabilitation

The aim of this study to investigate of novel assistive technologies based on serious gaming for the assessment of postural control and motor rehabilitation. Previous research already demonstrated that rehabilitation, assistive technologies, and physical activities can improve the quality of life of patients, and virtual reality applications may act as good additional companions during the therapeutic sessions. Indeed, workout routines supported by serious gaming encourage patients to train harder, bringing the therapy towards a pleasant game into a virtual environment, where specific goals must be reached. During the game, sensors track the movements of the players and transfer the data to software components that record a database from which patients’ progress can be determined. The acquired measures are interpreted in the form of new biomarkers, which enable the assessment of postural control. Such biomarkers are based on a probabilistic approach and show the capability to discriminate between well-performed exercises and incorrect movements. A long-term experimentation on a specific exercise is proposed, showing a considerable improvement, ranging from 5% to 30%, in the performance of patients .

The main contributions of this research are: i) the definition of new biomarkers for the postural assessment of patients affected by motor disorders; ii) the use of non-intrusive technologies, which enhances the freedom of movement for patients, increasing the reliability of the results; iii) the design of a virtual reality interface, which allows patients to interact in a pleasant and familiar environment without constant supervision; iv) the development of a new editor to easily customize virtual exercises, analyze rehabilitation progress, and create statistics.