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683 results on '"Longhi, Sauro"'

209. FaultBuster: data driven fault detection and diagnosis for industrial systems

Author

Bergantino, Nicola, Caponetti, Fabio, and Longhi, Sauro

Subjects
Statistical Process Control, Diagnostic inference, Articial intelligence, Fault detection
Abstract

Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks. Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while further investigations of the diagnostic system are still needed.

Published
2009

213. An adaptive VSS control for remotely operated vehicles.

Author

Dyda, Alexander A., Oskin, Dmitry A., Longhi, Sauro, and Monteriù, Andrea

Subjects
REMOTE submersibles control systems, ADAPTIVE control systems, SLIDING mode control, PARAMETER estimation, SWITCHING systems (Telecommunication)
Abstract

The paper is devoted to a new adaptive variable-structure system (VSS) control algorithm and its applications in underwater robotics. The proposed approach is based on the usage of sliding mode parameter (SMP) in order to have adaptation in VSS. It is demonstrated that the extreme values of SMPs are directly associated with high efficiency of transient processes in VSS. The essence of the presented adaptation algorithm consists on the adjustment of switching surface parameters such that the SMPs move from the assigned desirable value to the extreme values, with the overall effect to accelerate transients. The effectiveness of the derived approach is proven by applying it for controlling a real remotely operated vehicle. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2017
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231. Predictor Design for Altitude Control of a Seaweed Harvester

Author

Galllieri, Marco, Ringwood, John, Giantomassi, Andrea, Ippoliti, Gianluca, Longhi, Sauro, Galllieri, Marco, Ringwood, John, Giantomassi, Andrea, Ippoliti, Gianluca, and Longhi, Sauro

Abstract

In this paper, the predictor design, for altitude control of a seaweed harvester, is investigated. The harvesting system consists of a vessel and a suspended harvester device, the altitude of which is controlled by a winch. The control approach of Gallieri and Ringwood (2010), including a feedforward action, which requires a single step disturbance prediction, is investigated further, focusing on the disturbance prediction, for noisy sensors. The prediction is performed using AR and ARMA models, identified online, by using the Recursive Least Squared with Forgetting Factor (RLSFF) algorithm and the Kalman Filter (KF). The dependance between the error spectrum and the quality of the control is shown, and the prediction performances are evaluated, using an FFT-based criterion, oriented to the feedforward application. The control performances are then evaluated, and the results are compared to Gallieri and Ringwood (2010).

Published
2010

232. Probabilistic Self-Localization and Mapping: An Asynchronous Multirate Approach

Author

Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica, Ministero dell'Istruzione dell'Università e della Ricerca, Italia, Armesto, Leopoldo, Ippoliti, Gianluca, Longhi, Sauro, Tornero Montserrat, Josep, Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica, Ministero dell'Istruzione dell'Università e della Ricerca, Italia, Armesto, Leopoldo, Ippoliti, Gianluca, Longhi, Sauro, and Tornero Montserrat, Josep

Abstract

"© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.", [EN] In this paper, we present a set of robust and efficient algorithms with O(N) cost for the solution of the Simultaneous Localization And Mapping (SLAM) problem of a mobile robot. First, we introduce a novel object detection method, which is mainly based on multiple line fitting method for landmark detection with regular constrained angles. Second, a line-based pose estimation method is proposed, based on LeastSquares (LS). This method performs the matching of lines, providing the global pose estimation under assumption of known Data-Association. Finally, we extend the FastSLAM (FActored Solution To SLAM) algorithm for mobile robot self-localisation and mapping by considering the asynchronous sampling of sensors and actuators. In this sense, multi-rate asynchronous holds are used to interface signals with different sampling rates. Moreover, an asynchronous fusion method to predict and update mobile robot pose and map is also presented. In addition to this, FastSLAM 1.0 has been also improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution of the robot pose. This approach has a lower computational cost than the original Extended Kalman Filtering (EKF) approach in FastSLAM 2.0. All these methods have been combined in order to perform an efficient and robust self-localization and map building process. Additionally, these methods have been validated with experimental real data, in mobile robot moving on an unknown environment for solving the SLAM problem.

Published
2008

244. Visual Based Landing for an Unmanned Quadrotor.

Author

Cocchioni, Francesco, Frontoni, Emanuele, Ippoliti, Gianluca, Longhi, Sauro, Mancini, Adriano, and Zingaretti, Primo

Abstract

One of the main challenges for Unmanned Aerial Systems (UAVs) is to extend the endurance of small vehicles such as multi-rotors. Actually, Li-po batteries that guarantee a flight of about 20 min power this type of vehicles. The endurance can be extended by enabling vehicles to look for recharging station(s). In this paper, we propose a vision system able to detect and track a given pattern hosted on the target-landing platform. The pattern is also useful to estimate the UAV position while approaching the target or during the hovering close to the target. The paper focuses on an optimized adaptive thresholding technique that manages critical situations as changes in the scene's illumination / shadows. The developed system runs at 90 Hz for processing a 752×480 grayscale image. Preliminary results on an NVidia Tegra Jetson K1 platform are also presented to distribute the computation between CPU and GPU. [ABSTRACT FROM AUTHOR]

Published
2016
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