arXiv subjects

Angelo Cenedese

Publications and source records attributed to Angelo Cenedese.

At least 19 recordsLinked to original sources

arXiv2026

Towards safe and optimal flight: Viability Kernel MPC for Fully Actuated Multirotor

Industrial aerial robotics demands safety guarantees for navigation in unstructured environments while optimizing performance and computational efficiency. This paper presents a method for generating safe pose trajectories for fully actuated multirotors within a Model Predictive Control (MPC) framework, leveraging both viability theory and data-driven methods. Obstacle avoidance is enforced through dynamically computed axis-aligned bounding boxes, providing formal safety guarantees without exhaustive offline reachability analysis. Numerical simulations on a fully actuated tilted hexarotor validate the approach, demonstrating successful navigation in cluttered environments with real-time computational performance.

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arXiv2026

Towards Autonomous Commissioning of Industrial Drives via Multi-Objective Bayesian Optimization

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.

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arXiv2026

Dynamic Control Allocation for Dual-Tilt UAV Platforms

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.

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arXiv2026

Force Polytope-Based Cant-Angle Selection for Tilting Hexarotor UAVs

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.

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arXiv2026

RotorSuite: A MATLAB/Simulink Toolbox for Tilt Multi-Rotor UAV Modeling

In recent years, aerial platforms have evolved from passive flying sensors into versatile, contact-aware robotic systems, leading to rapid advances in platform design. Standard coplanar and collinear quadrotors have been complemented by modern tilted and tilting multi-rotor platforms with enhanced maneuverability. To properly analyze, control, and validate the performance of these emerging platforms, an accurate modeling step is required; however, this can be time-consuming, user-dependent and error-prone. To address this issue, we propose a MATLAB/Simulink toolbox for modeling and simulating the dynamics of a broad class of multi-rotor platforms through both an analytical and physics-based approaches. The toolbox, named RotorSuite, is provided with comprehensive documentation and example use cases, representing a valuable tool for didactic, research, and industrial development purposes.

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arXiv2025

Multi Agent Switching Mode Controller for Sound Source localization

Source seeking is an important topic in robotic research, especially considering sound-based sensors since they allow the agents to locate a target even in critical conditions where it is not possible to establish a direct line of sight. In this work, we design a multi- agent switching mode control strategy for acoustic-based target localization. Two scenarios are considered: single source localization, in which the agents are driven maintaining a rigid formation towards the target, and multi-source scenario, in which each agent searches for the targets independently from the others.

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arXiv2025

Trajectory control of a suspended load with non-stopping flying carriers

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.

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arXiv2024

A Sim-to-Real Vision-based Lane Keeping System for a 1:10-scale Autonomous Vehicle

In recent years, several competitions have highlighted the need to investigate vision-based solutions to address scenarios with functional insufficiencies in perception, world modeling and localization. This article presents the Vision-based Lane Keeping System (VbLKS) developed by the DEI-Unipd Team within the context of the Bosch Future Mobility Challenge 2022. The main contribution lies in a Simulation-to-Reality (Sim2Real) GPS-denied VbLKS for a 1:10-scale autonomous vehicle. In this VbLKS, the input to a tailored Pure Pursuit (PP) based control strategy, namely the Lookahead Heading Error (LHE), is estimated at a constant lookahead distance employing a Convolutional Neural Network (CNN). A training strategy for a compact CNN is proposed, emphasizing data generation and augmentation on simulated camera images from a 3D Gazebo simulator, and enabling real-time operation on low-level hardware. A tailored PP-based lateral controller equipped with a derivative action and a PP-based velocity reference generation are implemented. Tuning ranges are established through a systematic time-delay stability analysis. Validation in a representative controlled laboratory setting is provided.

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arXiv2024

Bearing-based Target Localisation in Search and Rescue Scenarios

This paper deals with the target localisation problem in search and rescue scenarios in which the technology is based on electromagnetic transceivers. The noise floor and the shape of the electromagnetic radiation pattern make this problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces with the inverse of the distance from the electromagnetic source thus impacting estimation-based techniques applicability. On the other hand, non-isotropic radiation patterns lessen the efficacy of gradient-based policies. In this work, we manage a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation. Simulations specialized in the ARTVA technology used in search and rescue in avalanche scenarios confirm that our scheme outperforms current solutions.

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arXiv2024

Star-shaped Tilted Hexarotor Maneuverability: Analysis of the Role of the Tilt Cant Angles

Star-shaped Tilted Hexarotors are rapidly emerging for applications highly demanding in terms of robustness and maneuverability. To ensure improvement in such features, a careful selection of the tilt angles is mandatory. In this work, we present a rigorous analysis of how the force subspace varies with the tilt cant angles, namely the tilt angles along the vehicle arms, taking into account gravity compensation and torque decoupling to abide by the hovering condition. Novel metrics are introduced to assess the performance of existing tilted platforms, as well as to provide some guidelines for the selection of the tilt cant angle in the design phase.

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arXiv2024

Smart Fleet Solutions: Simulating Electric AGV Performance in Industrial Settings

This paper explores the potential benefits and challenges of integrating Electric Vehicles (EVs) and Autonomous Ground Vehicles (AGVs) in industrial settings to improve sustainability and operational efficiency. While EVs offer environmental advantages, barriers like high costs and limited range hinder their widespread use. Similarly, AGVs, despite their autonomous capabilities, face challenges in technology integration and reliability. To address these issues, the paper develops a fleet management tool tailored for coordinating electric AGVs in industrial environments. The study focuses on simulating electric AGV performance in a primary aluminum plant to provide insights into their effectiveness and offer recommendations for optimizing fleet performance.

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arXiv2024

Integrated Hardware and Software Architecture for Industrial AGV with Manual Override Capability

This paper presents a study on transforming a traditional human-operated vehicle into a fully autonomous device. By leveraging previous research and state-of-the-art technologies, the study addresses autonomy, safety, and operational efficiency in industrial environments. Motivated by the demand for automation in hazardous and complex industries, the autonomous system integrates sensors, actuators, advanced control algorithms, and communication systems to enhance safety, streamline processes, and improve productivity. The paper covers system requirements, hardware architecture, software framework and preliminary results. This research offers insights into designing and implementing autonomous capabilities in human-operated vehicles, with implications for improving safety and efficiency in various industrial sectors.

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arXiv2023

An Active-Sensing Approach for Bearing-based Target Localization

Characterized by a cross-disciplinary nature, the bearing-based target localization task involves estimating the position of an entity of interest by a group of agents capable of collecting noisy bearing measurements. In this work, this problem is tackled by resting both on the weighted least square estimation approach and on the active-sensing control paradigm. Indeed, we propose an iterative algorithm that provides an estimate of the target position under the assumption of Gaussian noise distribution, which can be considered valid when more specific information is missing. Then, we present a seeker agents control law that aims at minimizing the localization uncertainty by optimizing the covariance matrix associated with the estimated target position. The validity of the designed bearing-based target localization solution is confirmed by the results of an extensive Monte Carlo simulation campaign.

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arXiv2023

Trajectory Tracking for Tilted Hexarotors with Concurrent Attitude Regulation

Tilted hexarotors embody a technology that remains partially unexploited in terms of its potential, especially concerning precise and concurrent position and attitude control. Focusing on these aerial platforms, we propose two control architectures that can tackle the trajectory tracking task, ensuring also the attitude regulation: one is designed resting on the differential flatness property of the system, which is investigated in the paper, and the other is a hierarchical nonlinear controller. We comparatively discuss the performance of the two control schemes, in terms of the accuracy of both the tracking control action and the attitude regulation, the input effort, and the robustness in the presence of disturbances. Numerical results reveal both the robustness of the hierarchical approach in the case of external disturbance and the accuracy of the differential flatness-based controller in unwindy conditions.

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arXiv2023

A Natural Indirect Adaptive Controller for a Satellite-Mounted Manipulator

The work considers the design of an indirect adaptive controller for a satellite equipped with a robotic arm manipulating an object. Uncertainty on the manipulated object can considerably impact the overall behavior of the system. In addition, the dynamics of the actuators of the base satellite are non-linear and can be affected by malfunctioning. Neglecting these two phenomena may lead to excessive control effort or degrade performance. An indirect adaptive control approach is pursued, which allows consideration of relevant features of the actuators dynamics, such as loss of effectiveness. Furthermore, an adaptive law that preserves the physical consistency of the inertial parameters of the various rigid bodies comprising the system is employed. The performance and robustness of the controller are first analyzed and then validated in simulation.

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arXiv2023

Tag-based Visual Odometry Estimation for Indoor UAVs Localization

The agility and versatility offered by UAV platforms still encounter obstacles for full exploitation in industrial applications due to their indoor usage limitations. A significant challenge in this sense is finding a reliable and cost-effective way to localize aerial vehicles in a GNSS-denied environment. In this paper, we focus on the visual-based positioning paradigm: high accuracy in UAVs position and orientation estimation is achieved by leveraging the potentials offered by a dense and size-heterogenous map of tags. In detail, we propose an efficient visual odometry procedure focusing on hierarchical tags selection, outliers removal, and multi-tag estimation fusion, to facilitate the visual-inertial reconciliation. Experimental results show the validity of the proposed localization architecture as compared to the state of the art.

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arXiv2023

Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems

This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics. In the literature, most of the results related to multi-agent formation tracking do not consider energy issues while investigating distributed feedback control laws. In order to account for this crucial design aspect, we contribute by formalizing and proposing a solution to an optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization, through a specific and key choice of potential functions in the optimization cost. To this end, we show how to compute the inverse dynamics in a centralized fashion by means of the Projector-Operator-based Newton's method for Trajectory Optimization (PRONTO) and, more importantly, we exploit such an offline solution as a general reference to devise a stabilizing online distributed control law. Finally, numerical examples involving a cubic formation following a chicane-like path in the 3D space are provided to validate the proposed control strategies.

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arXiv2023

A Robustness Analysis to Structured Channel Tampering Over Secure-by-Design Consensus Networks

This work addresses multi-agent consensus networks where adverse attackers affect the convergence performances of the protocol by manipulating the edge weights. We generalize (Fabris and Zelazo, 2022) and provide guarantees on the agents' agreement in the presence of attacks on multiple links in the network. A stability analysis is conducted to show the robustness to channel tampering in the scenario where part of the codeword, corresponding to the value of the edge weights, is corrupted. Exploiting the built-in objective coding, we show how to compensate the conservatism that may emerge because of multiple threats in exchange for higher encryption capabilities. Numerical examples related to semi-autonomous networks are provided.

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