Adriano Fagiolini

In systems where many heterogeneous agents operate autonomously with competing goals and without a centralized planner or global information repository, safety and performance can only be guaranteed by "social" rules imposed on the individual agents' behaviors. The nature of social rules is typically local, based on information made available to an agent from a small number of its neighbors. Examples of such regulated autonomy include car mobility with traffic rules and logistic robots in warehouses. Other systems, such as distributed power plants, are emerging rapidly. In these systems, detecting whether any agent is not abiding by the rules is crucial for raising alerts and taking appropriate countermeasures. However, the limited visibility due to the local nature of information makes misbehavior detection challenging for any single agent, and only through the exchange of information between agents can sufficient clues be gathered to arrive at a decision.

We consider threats posed to such a society by the misbehaviors of its members, whether due to faults or malice, and the possibility to detect and isolate them through cooperation among peers. We discuss intrusion detection algorithms that allow for the identification of deviance from these rules, and algorithms to build a consensus view on the environment and the integrity of peers, thereby improving the overall security of the robotic society. After providing a formal framework for describing social rules that unifies various applications, we study how to develop tools to automatically generate local monitors' code.

Soft robots are considered cutting-edge technology, primarily aimed at enabling safe and effective physical interactions between humans and robots. These robots are characterized by their ability to dynamically modulate elasticity during movement, which opens up numerous opportunities in everyday life by facilitating human-like abilities such as dexterity and robustness. To fully harness their potential, it is essential to accurately determine their stiffness, a parameter that is inherently difficult to measure. In this context, we first explore techniques for the estimation of stiffness and flexibility torque in robot joints. This challenge is approached from the motor side, treating the flexibility torque signal as an unknown input in the linear motor model. Unknown Input Observers (UIOs) are powerful tools used here, traditionally employed for detecting system failures and achieving correct state estimation despite unknown inputs.

Among soft robots, articulated types feature concentrated elasticity at the joints, which are actuated by Variable Stiffness Actuators (VSA). These devices are primarily driven by electric or pneumatic mechanisms, enabling precise position and velocity control while allowing for online compliance adjustments. In this realm, we investigate innovative solutions for adaptive control and learning for both articulated soft robots and continuum soft robots, comparing them to traditional approaches.

Continuum soft robots, with their inherent morphological flexibility and compliance, hold the promise of a disruptive impact across various fields. However, they pose challenges in modeling systems with theoretically infinite states. One possible strategy to tackle this complexity is to apply model-free machine learning techniques, treating the soft robot as a black box. Conversely, model-based methods that account for the infinite nature of the problem remain largely unfeasible. In addressing these challenges, we propose new, singularity- and discontinuity-free model parameterizations that link soft-bodied and rigid-bodied robots. This connection is crucial for developing numerically stable and well-defined controllers across the entire configuration space. We finally introduce robust closed-loop position controllers for soft-bodied robots, grounded in nonlinear adaptive control theory.

In the near future a great number of automotive applications will become possible, by leveraging on denser and faster communication networks also enabled by the 5G technology. Thanks to the Vehicle-to-Everything (V2X) architecture, vehicles, passengers, and pedestrians will be able to cooperatively plan and optimize their travel experience. They will be able to share evidence of possible hazards, including unexpected traffic jams in tunnels, road damages, anomalous behavior of human drivers and autonomous pilots, thus improving the overall safety of passengers and pedestrians.

In this scenario, the race towards (electric) vehicles with full self-driving capacity has just begun. However, several obstacles have to be overcome before this technology goes mainstream to the market, including infrastructure modernization, legislations definition, and stronger guarantees on the ability of an autonomous vehicle to detect and react to uncertainties caused by unexpected changes in the driving conditions. A notable example is the field of self-driving vehicles, where the DARPA Grand Challenge and Urban Challenge have pushed the robotics community to build autonomous cars for unstructured or urban scenarios. We investigate on and propose fast estimators) of the environmental conditions (road, wind, etc.) and suitable longitudinal and lateral controllers that can promptly react and ensure safety. We believe this research can strengthen the applicability of self-driving solutions and promote their usage in the society for safer roads.

Unmanned Aerial Vehicles (UAV) have been drawing increasing attention for more than two decades in various application fields, ranging from the industry to the military and from the service to the entertainment. Due to their ability to reach places, particularly those that are hardly accessible by land vehicles, UAVs are convenient tools for monitoring areas where natural disasters have just occurred. This is of high interest in the Pacific region, where remote neighborhoods need to be rapidly checked after cyclones or floods. The U.N. Food and Agriculture Organization (FAO) has launched in the Philippines a drone initiative to assess where agricultural land is at most risk of natural disasters and how to rapidly evaluate damages after they occur. It is strongly believed that the adoption of UAV platforms can significantly enhance risk and damage assessments, but also revolutionize the way to prepare for and respond to disasters. Using this kind of aircraft in hostile conditions, including strong wind gusts, is still an open problem. The nonlinearity of the system model, along with its underactuation, must taken into account, as they otherwise negatively affect on the mission performance, which is particularly true for lightweight low-cost quadrotors.

In this context, we study solutions that are applicable to low-cost multirotor aircraft, which allows avoiding direct wind speed measurement via anemometers. We seek solutions with the appealing features of being simple, having low computation cost, being able to obtain a fast response to wind gusts, and implementable on virtually all aircraft systems, as a stand-alone solution or an extension plugin for existing controllers. Along this line, we propose an innovative approach where wind disturbance is modeled as an unknown exogenous input and then it is estimated via an Unknown Input-State Observer (UIO). In order to further improve the promptness and efficacy of the controlled aircraft, we describe alternative solutions using Nonlinear UIOs and model-decoupling, ESO-based techniques which are also robust to model uncertainty.

A problem of great interest in real applications using large power machines and motors is reducing the number of sensors needed for processing a given control law. Motion control of systems with induction motors (IM) without speed sensor (sensorless) has been longly addressed by many authors, since these systems often operate in unaccessible environments. A crucial problem to solve for the implementation of sensorless control laws is the determination of both the rotor flux vector and the speed.

We investigate on new solutions to estimate the state of these machines and to control them despite the uncertainties of the model, with a special focus on low-speed operating conditions and with varying load conditions. As is well known, the observability property of the model is crucial for the existence of state observers, a property that is known to be lost at zero rotor speed. We provide a new approach to estimate the speed of an IM by using an extended Kalman filter (EKF), which remains valid at very low rotor speed. Also, we reformulate the motor model by using complex-valued variables, which allows reducing the size of the model as well as simplifying the observability property. This enables the derivation of Extended Complex-valued Kalman Filters (ECKF) whose main feature is a smaller required computational time.