Robotics research is faced within the automation group with a strong methodological approach, aiming at the thorough understanding of the foundational principles and theories. This is key for the development of identification, estimation and control techniques for modern applications in the fields of industrial and civil robotics: the study and the design of algorithms for manipulators and mobile platforms in these two areas find a further synthesis in the field of cooperative robotics, where network theory comes into play with multiagent robotic systems.
This research is focused on the modeling and control of terrestrial and aerial unmanned vehicles and ranges from the identification of the system to the design of control laws for autonomous operation of the vehicle both as a single unit and in cooperation with other agents of the same or different nature. Ongoing research activities include:
Focus of the research activity is the development of advanced modeling and control approaches for mechatronic systems, in an integrated approach where the interplay of components of different nature is taken into consideration since the early development stages. Integration of hardware and embedded software components is a specific feature of the design approach. The research activities include:
The research focuses on multi-agent robotics for exploration, map building and patrolling of unstructured and unknown environments. In particular, we are interested in understanding the benefits of the use of multiple cooperating robots in terms of improved localization from relative measurements, reduced time-to-map and scalability. Other fundamental aspects under investigation are robustness to unreliable communication and scalability. Ongoing research projects are the following:
This line of research focuses on quantum systems, their control and their applications in information technologies. In particular, we are interested in the study of models for quantum open systems that include noise and measurement processes, as well as open-loop and feedback control. The tasks of interest include robust state preparation, noise suppression and information encoding in physical systems. Key issues we consider include assessing scalability, speed and robustness of the control strategies. Ongoing research projects aim to study:
The research focuses on quantum systems, their control and the emerging applications in information technologies. In particular, we are interested in probabilistic models for quantum noise and quantum feedback, generation of entangled states (i.e. states that exhibit multi-system quantum correlations, not reproducible using classical variables) on networks of systems, and quantum walks on graphs. Key aspects include assessing scalabiltiy, speed and robustness of the control strategies. Ongoing research projects aim to study:
Spectral estimation is the science of building models in the frequency domain from measured data. Our research focuses on the development of spectral estimation techniques building models with high resolution in prescribed frequency bands.
The focus of this area is about developing methodological tools for enabling artificial systems to safely and efficiently operate without or limited need of human intervention. Application areas are the most disparate, ranging from automatic speech or visual recognition systems to robots that are able to interact with an unknown environment while performing assigned tasks, to advanced process control systems (such as, e.g. in the pretrolchemical industry) or biomedical applications such as neuroscience.
Computer vision is the science of retrieving information from images and movies (sequences of images indexed by time). Our goal is to develop methodologies to enable artificial systems to use visual sensors (such as cameras) to interact with the environment similarly to what us humans do.
Machine Learning is about endowing artificial systems the ability to learn from experience. Our research in this field spans a broad range of topics and includes building models from measured data as well as learning actions from "experience".
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