Research
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Camera Networks
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Rigidity Control & UAV Systems
Rigidity theory has emerged as an efficient tool in the control field of coordinated multi–agent systems, such as multi–robot formations and UAVs swarms that are characterized by sensing, communication and movement capabilities. This research is applied to the study of methodologies and the development of algorithms for estimation and control of autonomous aerial vehicles (UAVs). Applications of interest include:
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Nano Satellite Systems
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NETWORKED CONTROL SYSTEMS |
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Networked control systems are multiagent, multitask networks with limited resources. They employ cooperation and the distributed coordination of sensors/actuators to fulfill complex tasks that are not possible to a single agent.
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ADVANCED CONTROL APPLICATIONS |
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Brain and Biological Networks
Recently, considerable attention has been devoted to the study of the human brain as a network of different cortical regions that show coherent activity during resting-state: we study how synchronization patterns do emerge in such a complex network and how to model it for a possible control action design. A parallel activity concerns the analysis and the modeling of the dynamics of mitochondria, multi-functional organelles that have a central role for cell life: these, again, show population emergent behaviors that are designed to fulfill complex tasks for the whole organism.
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Time series analysis for event detection and classification Machine learning approach to gesture recognition: With the aim of monitoring the human activity, wearable devices provide an enhanced usability and a seamless human experience with respect to other portable devices (e.g. smartphones) in critical tasks as well as in leisure and sport activities: algorithmic solution especially tackled for these devices need to be developed so as to provide detection/identification accuracy and efficiency. Data mining and model learning from sensor network measurements: To fully unleash the potential of the IoT and CPS paradigms, however, advanced data processing techniques are required to extract useful information from heaps of raw data: we focus on the problem of processing time series of data, with the twofold aim of: i) extracting recurrent patterns, which represent the normal (i.e., expected) behavior of the monitored parameters, and ii) detecting anomalies, i.e., significant deviations from the normal behavior. |
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Fusion Plasmas Modeling and Control [videos]
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