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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Kaouther. Belkilani Afef. Ben Othman Mongi. Besbes Robotics Informatics and Complexes Systems Laboratory LR16ES07 (RISC) Higher Institute of Information and Communication Technologies University of Carthage Bizerte Tunis Robotics Informatics and Complexes Systems Laboratory LR16ES07 (RISC) National Engineering School of Carthage University of Carthage Tunis Tunis Robotics Informatics and Complexes Systems Laboratory LR16ES07 (RISC). Higher Institute of Information and Communication Technologies University of Carthage Tunis Tunis

ISBN: (数字)9781728169996

ISBN: (纸本)9781728170008

Having enough knowledge of the basic solar components, including Direct Normal Irradiation (DNI), Diffuse Horizontal Irradiance (DHI) and Global Horizontal Irradiation (GHI), is greatly significant to concentrated solar power (CSP) projects. (DNI), the amount of direct irradiance received on a plane normal to the sun, is of remarkable usefulness to CSP technologies around the world. However, in Tunisia, the field of solar energy generation faces twofold challenges. First, the unique type of solar concentrating collectors works only on global horizontal irradiation (GHI) which is the geometric sum of DNI and DHI. Second, these collectors are available in a limited number of *** this additional review of hourly DNI, our approach to the real time assessment of the clear sky DNI gives a good estimation in the worst case the mean absolute error equal to 40 W/m 2 for hourly values in comparison to the measured data.

关键词： Numerical models Atmospheric modeling Atmospheric measurements Mathematical model Radiation effects Estimation Indexes

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Finite-time Frequency Estimator for Harmonic Signal

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IFAC-PapersOnLine 2020年第2期53卷 584-589页

作者： Alexey A. Bobtsov Anastasiia O. Vediakova Nikolay A. Nikolaev Olga V. Slita Anton A. Pyrkin Alexey A. Vedyakov Department of Control Systems and Robotics ITMO University Saint Petersburg Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Applications and Systems Saint Petersburg State University Saint Petersburg Russia

This paper is devoted to a frequency estimation of a pure sinusoidal signal in finite-time. The parameterization is based on applying delay operators to a measurable signal. The result is the first-order linear regression model with one parameter, which depends on the signal frequency. The proposed method of finite-time estimation consists of two steps. On the first step, the standard gradient descent method is used to estimate the regression model parameter. On the next step using algebraic equations, finite-time frequency estimate is found. The described method does not require measuring or calculating derivatives of the input signal and uses one integrator for the gradient method and another one for the finite-time estimation. The efficiency of the proposed approach is demonstrated through the set of numerical simulations.

关键词： parameter estimation observers frequency estimation finite-time estimation

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Human Intention Recognition for Human Aware Planning in Integrated Warehouse systems

Human Intention Recognition for Human Aware Planning in Inte...

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Mediterranean Conference on Control and Automation (MED)

作者： Tomislav Petković Jakub Hvězda Tomáš Rybecký Ivan Marković Miroslav Kulich Libor Přeučil Ivan Petrović Faculty of Electrical Engineering and Computing University of Zagreb Laboratory for Autonomous Systems and Mobile Robotics Unska 3 Zagreb Croatia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9781728157429

ISBN: (纸本)9781728157436

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned; however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters.

关键词： Robot kinematics Frequency modulation Planning Task analysis Hidden Markov models Trajectory

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Corrigendum to "Oppositely-charged silver nanoparticles enable selective SERS molecular enhancement through electrostatic interactions" [Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 322 (2024) 124852]

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Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy 2025年 339卷 126218页

作者： Yuqing Gu Siyi Wu Zhewen Luo Linley Li Lin Jian Ye State Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 PR China. State Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 PR China. Electronic address: linli92@***. State Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 PR China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai 200240 PR China Shanghai Key Laboratory of Gynecologic Oncology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai PR China. Electronic address: yejian78@***.

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Bayesian deep multi-instance learning for student performance prediction based on campus big data

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Neurocomputing 2025年 647卷

作者： Huang, Jiayang Yang, Keyi Wang, Quan Yang, Pengfei Ruan, Ziling Wang, Jiaxi Zhang, Zhi-Qiang School of Computer Science and Technology Xidian University Xi'an710126 China The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi'an710126 China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds Leeds United Kingdom

Problem: Predicting student performance using campus big data and deep learning methods has emerged as a promising alternative to traditional psychological assessments, which are often delayed and subjective. However, existing deep learning approaches face significant challenges, including the sparsity of campus big data and susceptibility to overfitting. Objective: To address these issues, this study aims to develop a robust and effective framework for predicting student performance by overcoming data sparsity and overfitting problems. Method: We propose a novel Bayesian Deep Multi-Instance Learning (Bay-DeepMIL) framework. The method integrates multi-instance learning (MIL) to handle data sparsity and incorporates a Bayesian framework to treat network parameters as probabilistic distributions, enhancing robustness and mitigating overfitting. Additionally, a Bayesian multi-head attention mechanism is introduced to dynamically assign importance to different instances, improving the extraction of key information from multi-instance data. Results: Extensive experiments demonstrate that Bay-DeepMIL outperforms state-of-the-art methods in prediction accuracy. The framework also provides uncertainty estimates for each prediction, offering valuable confidence measures and decision support for educational stakeholders. Conclusion: The proposed Bay-DeepMIL framework not only advances the technical capabilities of predictive models but also provides practical tools for enhancing educational decision-making. This study underscores the effectiveness of integrating Bayesian inference with multi-instance learning to address core challenges such as data sparsity and model overfitting in student performance prediction. © 2025 Elsevier B.V.

关键词： Students

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： robotics

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LMI-based Optimization for the Position Feedback Control of Underactuated Robotic systems via an Affine PD Controller: Case of the Pendubot

LMI-based Optimization for the Position Feedback Control of ...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Sahar Jenhani Hassène Gritli Giuseppe Carbone Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Le Belvédère Tunis Tunisia Higher Institute of Information and Communication Technologies & RISC Lab (LR16ES07) University of Carthage Borj Cedria Tunis Tunisia Department of Mechanical Energy and Management Engineering University of Calabria Via Bucci Cubo 46C Rende (Cs) Italy

ISBN: (纸本)9781665490597

The set-point control problem of underactuated mechanical systems is a challenging issue and needs to be further investigated. This research work focuses on such problem for underactuated Lagrangian-type robotic systems, and then the objective lies in designing a suitable controller to change the robot’s current position to a desired state. Thus, our main idea is to control the robotic systems through an affine-type PD controller and by virtue of the LMI-based optimization techniques. Thus, we will develop LMI conditions on the controller gains to ensure the stability of the underactuated robot at the desired state. Then, to achieve this goal, we will consider the rescaled dynamical model describing the difference between the nonlinear model and its approximate linear dynamics, where we assume that this rescaled dynamics satisfies such some Lipschitz condition. Finally, the pendubot manipulator is adopted in order to verify the efficiency of the proposed controller.

关键词： Industries Simulation Friction Position control Stability analysis Nonlinear dynamical systems Mechanical systems

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PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains

arXiv

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arXiv 2022年

作者： Dey, Sharmita Fan, David Schmid, Robin Dixit, Anushri Otsu, Kyohei Touma, Thomas Schilling, Arndt F. Agha-Mohammadi, Ali-Akbar NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States The University of Goettingen University Medical Center Goettingen Germany The Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA United States Zurich8092 Switzerland Control and Dynamical Systems California Institute of Technology PasadenaCA United States Mechanical and Civil Engineering California Institute of Technology PasadenaCA United States

Legged robots can traverse a wide variety of terrains, some of which may be challenging for wheeled robots, such as stairs or highly uneven surfaces. However, quadruped robots face stability challenges on slippery surfaces. This can be resolved by adjusting the robot's locomotion by switching to more conservative and stable locomotion modes, such as crawl mode (where three feet are in contact with the ground always) or amble mode (where one foot touches down at a time) to prevent potential falls. To tackle these challenges, we propose an approach to learn a model from past robot experience for predictive detection of potential failures. Accordingly, we trigger gait switching merely based on proprioceptive sensory information. To learn this predictive model, we propose a semi-supervised process for detecting and annotating ground truth slip events in two stages: We first detect abnormal occurrences in the time series sequences of the gait data using an unsupervised anomaly detector, and then, the anomalies are verified with expert human knowledge in a replay simulation to assert the event of a slip. These annotated slip events are then used as ground truth examples to train an ensemble decision learner for predicting slip probabilities across terrains for traversability. We analyze our model on data recorded by a legged robot on multiple sites with slippery terrain. We demonstrate that a potential slip event can be predicted up to 720 ms ahead of a potential fall with an average precision greater than 0.95 and an average F-score of 0.82. Finally, we validate our approach in real-time by deploying it on a legged robot and switching its gait mode based on slip event detection. © 2022, CC BY-NC-SA.

关键词： Robots

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Mechanical Design and Control of an Underwater Robot for Pitch and Roll Stability 3

Mechanical Design and Control of an Underwater Robot for Pit...

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3rd International Conference on Advanced systems and Emergent Technologies, IC_ASET 2019

作者： Hachicha, Saber Zaoui, Chiheb Rabhi, Yassine Dallagi, Habib Nejim, Samir Maalej, Aref Automatic Control Marine Robotics Naval Academy Tunisia Laboratory of Signal Image and Energy Mastery National Higher School of Engineering of Tunis Tunisia Laboratory of Electromechanical Systems University of Sfax Tunisia

ISBN: (纸本)9781728113173

The use of the underwater robot for ship hull cleaning is increasingly studied given the valuable operational, economic and environmental advantages that it offers. The Automatic Control and Marine robotics Unit, within the Tunisian Naval Academy, has suggested a new underwater robot design that is equipped with two manipulating arms. The offered solution allows to combine a system portability with the improved cleaning efficiency offered by the manipulator arms. However, during underwater operation, the robot, located beneath the ship's hull, is exposed to several perturbation forces. These forces are mainly caused by water current and waves. This research focuses on the robot static and dynamic stability when it is exposed to external perturbations. The proposed approach to ensure the robot stability consists in improving its mechanical design. Therefore, we developed the design of the scaled robot to eliminate static behavior. The dynamic model was established and the electronic architecture was described. Besides, the control scheme was proposed. In conclusion, our study demonstrates the importance of the mechanical symmetry design and the control law to ensure the stability of the underwater robot. © 2019 IEEE.

关键词： Robots

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Practical fractional-order variable-gain super-twisting control with application to wafer stages of photolithography systems

arXiv

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arXiv 2021年

作者： Kuang, Zhian Sun, Liting Gao, Huijun Tomizuka, Masayoshi Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Mechanical Control System Lab Mechanical Engineering Department University of California BerkeleyCA94720 United States

In this paper, a practical fractional-order variable-gain super-twisting algorithm (PFVSTA) is proposed to improve the tracking performance of wafer stages for semiconductor manufacturing. Based on the sliding mode control (SMC), the proposed PFVSTA enhances the tracking performance from three aspects: 1) alleviating the chattering phenomenon via super-twisting algorithm and a novel fractional-order sliding surface (FSS) design, 2) improving the dynamics of states on the sliding surface with fast response and small overshoots via the designed novel FSS and 3) compensating for disturbances via variable-gain control law. Based on practical conditions, this paper analyzes the stability of the controller and illustrates the theoretical principle to compensate for the uncertainties caused by accelerations. Moreover, numerical simulations prove the effectiveness of the proposed sliding surface and control scheme, and they are in agreement with the theoretical analysis. Finally, practice-based comparative experiments are conducted. The results show that the proposed PFVSTA can achieve much better tracking performance than the conventional methods from various perspectives. Copyright © 2021, The Authors. All rights reserved.

关键词： Sliding mode control

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