A plethora of natural and artificial systems consists of the interconnection of basic units, where the global, observable behavior of the system is much richer than the behavior of its constituents and cannot be easily predicted starting from observations at the unit level. Relevant examples are the spreading of epidemics in populations, the diffusion of innovation in socio-technical systems, the flocking of birds and schooling of fish, the coordination of autonomous vehicles. We are engaged in characterizing, modeling, and controlling such systems, both from a theoretical and an applicative point of view.  We are at present focusing on epidemic spreading in heterogeneous networks with time-varying connections, and on the effect of individual behavioral traits on the global response of the system.

While many of the issues in modeling and control of industrial and mobile robots have been solved, there is still need of coordination and cooperation strategies to steer complex actions in uncertain fields, such as agriculture, disaster relief, manipulation in coordinated environments, surveillance. Robots are very often called to operate in harsh and unstructured environments, providing robustness to communication shortages or other faults. Distributed approaches can provide effective solutions, whereby robots should collectively and spontaneously agree on coordinated operation plans, mainly through peer-to-peer communication, without the need of a centralized coordination unit that may be overloaded and constitute a single point of failure. Our group is involved in several research projects that aim at coordinating in an efficient and distributed way robotic agents of heterogeneous nature. A particular focus has been recently given to heterogeneous teams of manned and unmanned robots or vehicles.

The big-data and machine learning hype has not left the systems and control community unaffected. Many modeling and control strategies based solely on data have been devised by several research groups. Although impressive results have been achieved by the community, most of the high-performance black-box controllers in diverse fields lack interpretability, since they prescind from the physical laws that regulate the phenomena. Our group aims at bridging this gap, by devising hybrid controllers based on an effective interplay of physical modeling and data-driven techniques, with particular focus on robotic and mechatronic systems. 

The revolution in everyday individual mobility is under everyone’s eye and is proceeding at high speed. Shared services of different nature (scooters, bicycles, electric and traditional cars), long-term rentals, car-pooling services, fully electric and plug-in electric vehicles are revolutionizing our lifestyle. With the debut of autonomous cars in view and the consequent coexistence of manned and unmanned vehicles in our streets, unexplored complex scenarios must be managed, involving technological, social, economic, behavioral, and psychological issues. Our research group is pursuing several research avenues in this new fascinating landscape, joining rigorous modeling and control theory with the analysis of transport data and human behavioral traits, toward the solution of complex problems. Relevant examples are the design of rebalancing strategies for shared vehicles, coordinated control strategies for manned and unmanned vehicles, control of smart intersections, to name a few.