Currently, I am a Ph.D. student in Engineering in Computer Science at the Department of Computer, Control, and Management Engineering "Antonio Ruberti" (DIAG) at University of Rome, "La Sapienza".
I achieved my bachelor degree in Engineering in Computer Science at University of Siena (2009/2010-2011/2012), and then a moved to Rome, where I finished my master degree in Artificial Intelligence and Robotics with the maximum grade (2012/2013-2013/2014). During my master studies I have participated, as a member of the team, to the RoboCup competitions. My main focus during this experience was the analysis and development of new coordination strategies to enable robots to operate collectively and effectively. To this end, I contributed to the formalization of a context-aware coordination system to augment robot knowledge with high-level information coming from the environment. Afterwards, I extended such an approach to the case of multi-robot search for non-adversarial targets in indoor human-populated environments. In 2014, I have been promoted to team leader to guide the team during the competitions of both RoboCup 2014 in Joao Pessoa (Brazil) as master student, and RoboCup 2015 in Hefei (China) as PhD student. Moreover, during the first year of PhD, I started to formalize my research within the context of service robotics. I have attended both a winter school in Orebro (Sweden) and a summer school in Lincoln (UK) where the focus was to enable AI techniques directly on operating robots. In fact, the aim of my research is to allow a robotic system to exploit state-of-art AI techniques in order to improve its performance. Such an integration is possible only if the robot can feature a suitable representation of the world. For these reasons, my research focuses on the problem of representation knowledge for autonomous robots, and especially on the problem of representing 'environmental semantic knowledge'. Recently, robots started to operate in common indoor scenarios. However, they are not yet aware of how to interpret the surrounding environment and when to modify it. Thus, my research addresses the problem of semantically annotating the space of the environment in order to improve robot services. Such a problem is known as "Spatial Semantic Mapping".