Angelo Cenedese

Giulia Michieletto, Angelo Cenedese, Luca Zaccarian et al.

We consider the hovering control problem for a class of multi-rotor aerial platforms with generically oriented propellers. Given the intrinsically coupled translational and rotational dynamics of such vehicles, we first discuss some assumptions for the considered systems to reject torque disturbances and to balance the gravity force, which are translated into a geometric characterization of the platforms that is usually fulfilled by both standard models and more general configurations. Hence, we propose a control strategy based on the identification of a zero-moment direction for the applied force and the dynamic state feedback linearization around this preferential direction, which allows to asymptotically stabilize the platform to a static hovering condition. Stability and convergence properties of the control law are rigorously proved through Lyapunov-based methods and reduction theorems for the stability of nested sets. Asymptotic zeroing of the error dynamics and convergence to the static hovering condition are then confirmed by simulation results on a star-shaped hexarotor model with tilted propellers.

Angelo Cenedese, Marco Michielan, Federico Tramarin et al.

Recently, different communities in computer science, telecommunication and control systems have devoted a huge effort towards the design of energy efficient solutions for data transmission and network management. This paper collocates along this research line and presents a novel energy efficient strategy conceived for Ethernet networks. The proposed strategy combines the statistical properties of the network traffic with the opportunities offered by the IEEE 802.3az amendment to the Ethernet standard, called Energy Efficient Ethernet (EEE). This strategy exploits the possibility of predicting the incoming traffic from the analysis of the current data flow, which typically presents a self-similar behavior. Based on the prediction, Ethernet links can then be put in a low power consumption state for the intervals of time in which traffic is expected to be of low intensity. Theoretical bounds are derived that detail how the performance figures depend on the parameters of the designed strategy and scale with respect to the traffic load. Furthermore, simulations results, based on both real and synthetic traffic traces, are presented to prove the effectiveness of the strategy, which leads to considerable energy savings at the cost of only a limited bounded delay in data delivery.

Giulia Michieletto, Angelo Cenedese, Daniel Zelazo

This work focuses on the bearing rigidity theory, namely the branch of knowledge investigating the structural properties necessary for multi-element systems to preserve the inter-units bearings when exposed to deformations. The original contributions are twofold. The first one consists in the definition of a general framework for the statement of the principal definitions and results that are then particularized by evaluating the most studied metric spaces, providing a complete overview of the existing literature about the bearing rigidity theory. The second one rests on the determination of a necessary and sufficient condition guaranteeing the rigidity properties of a given multi-element system, independently of its metric space.

David Petrovic, Gian Antonio Susto, Angelo Cenedese

The commissioning of industrial electric drives still relies heavily on manual tuning of cascaded control loops, requiring expert knowledge and significant time. In this paper, we propose a fully automated approach for tuning the current control loop of industrial drives using Bayesian Optimization (BO) directly on real hardware, without requiring a system model or firmware modifications. The drive is treated as a black-box system, and the controller parameters are iteratively updated through closed-loop experiments. The tuning problem is formulated as a multi-objective optimization task that directly minimizes tracking error, time-weighted error, overshoot, and oscillatory behavior, enabling the identification of Pareto-optimal controller configurations. To address discrete parameters, noisy evaluations, and limited budgets, we adopt a multivariate Tree-structured Parzen Estimator (TPE) as the underlying BO strategy. The proposed method operates under practical industrial constraints, including communication latency and limited evaluation budgets. The experimental validation on a real motor drive system under no-load conditions shows that the method achieves performance comparable to expert tuning within a few minutes and without human intervention. Results show that Gaussian Process (GP)-based BO can yield highly competitive final solutions, but TPE-based BO is better aligned with this setting due to faster convergence, richer Pareto-front approximation, and lower computational overhead.

Sofia Girardello, Giulia Michieletto, Angelo Cenedese et al.

This work presents the first closed-loop control framework for cooperative payload transportation with non-stopping flying carriers. The proposed method includes a feedback wrench-controller that actively regulates the load's pose by computing the wrench required for tracking its desired pose trajectory. Building upon grasp-matrix formulation and internal force redundancy, an optimization layer dynamically shapes internal-force parameters to guarantee persistent carrier motion, while not altering the desired load wrench. The desired non-stopping carrier's trajectories are computed using the system's kinematics and desired cable forces. Numerical simulations demonstrate that the method successfully prevents the carriers from stopping, while achieving a successful tracking of the desired load trajectory.

Alberto Piccina, Massimiliano Bertoni, Angelo Cenedese et al.

From a maneuverability perspective, the main advantage of tilting multirotor UAVs lies in the dynamic variability of the feasible executable wrench, which represents a key asset for physical interaction tasks. Accordingly, cant-angle selection should be optimized to ensure high performance while avoiding abrupt variations and preserving real-world feasibility. In this context, this work proposes a lightweight control framework for star-shaped interdependent cant-tilting hexarotor UAVs performing interaction tasks. The method uses an offline-computed look-up table of zero-moment force polytopes to identify feasible cant angles for a desired control force and select the optimal one by balancing efficiency and smoothness. The framework is integrated with a geometric full-pose controller and validated through Monte Carlo simulations in MATLAB/Simulink and compared against a baseline strategy. The results show a significant reduction in computation time, together with improved pose-tracking performance and competitive actuation efficiency. A final physics-based simulation of a complete wall inspection task in Simscape further confirms the feasibility of the proposed strategy in interacting scenarios.

Marcello Sorge, Federico Ciresola, Giulia Michieletto et al.

This paper focuses on dynamic control allocation for a hexarotor UAV platform, considering a trajectory tracking task as as case study. It is assumed that the platform is dual-tilting, meaning that it is able to tilt each propeller independently during flight, along two orthogonal axis. We present a hierarchical control structure composed of a high-level controller generating the required wrench for the tracking task, and a control allocation law ensuring that the actuators produce such wrench. The allocator imposes desired first-order dynamics on the actuators set, and exploits system redundancy to optimize the actuators state with respect to a given objective function. Unlike other studies on the subject, we explicitly model actuator saturation and provide theoretical insights on its effect on control performances. We also investigate the role of propeller tilt angles, by imposing asymmetric shapes in the objective function. Numerical simulations are presented to validate the allocation strategy.

Luca Varotto, Angelo Cenedese, Andrea Cavallaro

Active Position Estimation (APE) is the task of localizing one or more targets using one or more sensing platforms. APE is a key task for search and rescue missions, wildlife monitoring, source term estimation, and collaborative mobile robotics. Success in APE depends on the level of cooperation of the sensing platforms, their number, their degrees of freedom and the quality of the information gathered. APE control laws enable active sensing by satisfying either pure-exploitative or pure-explorative criteria. The former minimizes the uncertainty on position estimation; whereas the latter drives the platform closer to its task completion. In this paper, we define the main elements of APE to systematically classify and critically discuss the state of the art in this domain. We also propose a reference framework as a formalism to classify APE-related solutions. Overall, this survey explores the principal challenges and envisages the main research directions in the field of autonomous perception systems for localization tasks. It is also beneficial to promote the development of robust active sensing methods for search and tracking applications.

Luca Varotto, Angelo Cenedese

Research on connected vehicles represents a continuously evolving technological domain, fostered by the emerging Internet of Things (IoT) paradigm and the recent advances in intelligent transportation systems. Nowadays, vehicles are platforms capable of generating, receiving and automatically act based on large amount of data. In the context of assisted driving, connected vehicle technology provides real-time information about the surrounding traffic conditions. Such information is expected to improve drivers' quality of life, for example, by adopting decision making strategies according to the current parking availability status. In this context, we propose an online and adaptive scheme for parking availability mapping. Specifically, we adopt an information-seeking active sensing approach to select the incoming data, thus preserving the onboard storage and processing resources; then, we estimate the parking availability through Gaussian Process Regression. We compare the proposed algorithm with several baselines, which attain inferior performance in terms of mapping convergence speed and adaptivity capabilities; moreover, the proposed approach comes at the cost of a very small computational demand.

Luca Varotto, Angelo Cenedese

Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and probabilistic models to control the perception process of robotic sensing platforms. MMPAS is successfully employed in environmental exploration, collaborative mobile robotics, and target tracking, being fostered by the high performance guarantees on autonomous perception. In this context, we propose a bi-Radio-Visual PAS scheme to solve the transmitter discovery problem. Specifically, we firstly exploit the correlation between radio and visual measurements to learn a target detection model in a self-supervised manner. Then, the model is combined with antenna radiation anisotropies into a Bayesian Optimization framework that controls the platform. We show that the proposed algorithm attains an accuracy of 92%, overcoming two other probabilistic active sensing baselines.

Andrea Favrin, Vladislav Nenchev, Angelo Cenedese

While automated driving technology has achieved a tremendous progress, the scalable and rigorous testing and verification of safe automated and autonomous driving vehicles remain challenging. This paper proposes a learning-based falsification framework for testing the implementation of an automated or self-driving function in simulation. We assume that the function specification is associated with a violation metric on possible scenarios. Prior knowledge is incorporated to limit the scenario parameter variance and in a model-based falsifier to guide and improve the learning process. For an exemplary adaptive cruise controller, the presented framework yields non-trivial falsifying scenarios with higher reward, compared to scenarios obtained by purely learning-based or purely model-based falsification approaches.

Andrea Masiero, Angelo Cenedese

Marker-based motion capture (MoCap) systems can be composed by several dozens of cameras with the purpose of reconstructing the trajectories of hundreds of targets. With a large amount of cameras it becomes interesting to determine the optimal reconstruction strategy. For such aim it is of fundamental importance to understand the information provided by different camera measurements and how they are combined, i.e. how the reconstruction error changes by considering different cameras. In this work, first, an approximation of the reconstruction error variance is derived. The results obtained in some simulations suggest that the proposed strategy allows to obtain a good approximation of the real error variance with significant reduction of the computational time.