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engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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Efficient Deep Learning Modalities for Object Detection from Infrared Images

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Computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDM SPIHT IoT

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Daniil Privalov Denis Semenov Dmitry Lukichev Zhitao Liu Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems & Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than $\mathbf{9 9 \%}$. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance.

关键词： Training Vibrations Fault diagnosis Machine learning algorithms Fault detection Machine learning Prediction algorithms

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Machine Learning-Based Underwater Communication System Using Light Carrying Orbital Angular Momentum

Machine Learning-Based Underwater Communication System Using...

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CLEO: Science and Innovations, S and I 2022

作者： Jarrett, William A. Avramov-Zamurovic, Svetlana Esposito, Joel M. Nelson, Charles Weapons Robotics and Control Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States Electrical and Computer Engineering Department United States Naval Academy 597 McNair Rd AnnapolisMD21401 United States

ISBN: (纸本)9781557528209

An underwater communication system using light carrying orbital angular momentum is evaluated using a convolutional neural network through simulated and experimental thermally-generated underwater optical turbulence. 100% classification is demonstrated in no or weak turbulence. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Optical communication

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An Augmented and Diminished Reality Exergame for Investigating the Embodiment of a Prosthetic Limb

An Augmented and Diminished Reality Exergame for Investigati...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： MacAluso, Roberta Calandra, Davide Pratticò, Filippo Gabriele Battegazzorre, Edoardo Baiocchi, Gianmarco Boccardo, Nicolò Laffranchi, Matteo Bottino, Andrea Barresi, Giacinto Lamberti, Fabrizio Politecnico di Torino Department of Control and Computer Engineering Italy Rehab Technologies Lab Open University Affiliated Research Centre Istituto Italiano di Tecnologia Genoa Italy Istituto Italiano di Tecnologia Rehab Technologies Lab Genoa Italy Uwe Bristol Robotics Laboratory Bristol United Kingdom

ISBN: (纸本)9798331514846

Bionic limb prosthetics continue to face significant challenges in terms of artificial limb embodiment, often leading to prosthesis abandonment in patients. Following recent results in literature on digital trainings, this study investigates the impact of the visuo-attentive feedback of an Augmented/Diminished Reality (AR/DR) exergame on prosthetic embodiment. Two interaction paradigms were compared, i.e. a reference unrestricted interaction of a virtual prosthetic limb against a one in which the prosthesis and its interaction capability fades over time and requires the participant's visual-attention to reestablish it. Preliminary findings on non-amputees demonstrated hardship with the visuo-attentive approach which was associated with detrimental effects on the prosthesis embodiment, possibly because of the re-allocation of cognitive resources that are necessary to establish the process of body ownership. © 2025 IEEE.

关键词： Artificial limbs

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Transfer Learning: An Approach on the Riemannian Manifold for Multiclass Motor Imagery EEG Brain-Computer Interface

Transfer Learning: An Approach on the Riemannian Manifold fo...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Xu Sun Yaqi Chu Yang Luo Xingang Zhao Department of Control Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation (SIA) Chinese Academy of Sciences (CAS) Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences (CAS) Shenyang China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Motor imagery (MI) EEG-based brain-computer interface (BCI) facilitates direct communication between the brain’s intentions and a computer. In essence, it allows a person to control external equipment by decoding EEG signals during the mental imagery of limb movements. However, due to the intricate individual variances in EEG signals, creating a general decoding model with parameters applicable to all subjects is exceptionally challenging. Consequently, a prolonged calibration process is necessary to gather labeled subject-specific data for each individual, making it less user-friendly. A potential solution to this problem is transfer learning, a methodology that transfers knowledge from related domains to a target domain. To tackle the problem, this paper proposes a novel transfer learning approach on the Riemannian manifold framework in the context of multiclass MI EEG-based BCI classification. Particularly, a user-specific frequency band selection (FBS) method with MI EEG class distinctiveness is utilized to improve the accuracy and efficiency of Riemannian space calculations, which is measured using the inter-class distance and intra-class variance on the manifold. Then, the Riemannian space Alignment (RA) strategy is used to calibrate the MI EEG variances of different subjects. The comparative experiments between the proposed approach and baseline/conventional methods are conducted on a public dataset with four-class motor imagery EEG, including two-class transfer learning scenario and four-class transfer learning scenario. proposed transfer learning approach is outperformed. Compared with the baseline/conventional methods using a fixed wide frequency band, the preliminary results suggests that the proposed approach can significantly improve the transfer learning performance for the MI EEG signals from different subjects in Riemannian space.

关键词： Manifolds Mechatronics Machine vision Transfer learning Motors Particle measurements Electroencephalography Brain-computer interfaces Frequency measurement Decoding

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Reinforcement learning with data augmentation for lane change decision-making

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Journal of Institute of control, robotics and Systems 2021年第8期27卷 572-577页

作者： Kim, Min-Seong Eoh, Gyuho Park, Tae-Hyoung Department of Control Robot Engineering Chungbuk National University Korea Republic of Industrial AI Research Center Chungbuk National University Korea Republic of Department of Intelligent Systems and Robotics Chungbuk National University Korea Republic of

In a lane change decision-making technique based on RL (Reinforcement Learning), most data is related to keeping a lane;on the contrary, the data related to lane change is sparse. Consequently, an ego-vehicle fails to learn its policy. To overcome this issue, we propose a new RL-based lane change decision-making technique that uses a safety inspection module and augmented data. The safety inspection module restrains the collision of the ego-vehicle and generates virtual augmented data acquired from lane change situations. The introduction of virtual augmented data mitigates sparse lane-change data problems, which results in performance improvement. We simulated the proposed technique using an open-source simulator called CARLA. © ICROS 2021.

关键词： Decision making

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Prototype of Turbine Propulsion System with controllable Thrust Vector for Long-Range Unmanned VTOL Aircraft 21st

Prototype of Turbine Propulsion System with Controllable Thr...

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21st Polish control Conference, PCC 2023

作者： Czyba, Roman Lemanowicz, Marcin Domin, Jaroslaw Grzybowski, Jacek Stępień, Izabela Ungier, Lukasz Rosiek, Radoslaw Bialas, Piotr Niemiec, Michal Department of Automatic Control and Robotics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland Department of Chemical Engineering and Process Design Faculty of Chemistry Silesian University of Technology ul. Ks. M. Strzody 7 Gliwice44-100 Poland Department of Mechatronics Faculty of Electrical Engineering Silesian University of Technology ul. Akademicka 10a Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The aim of the project was to build a prototype of a turbine drive with a controllable thrust vector for a long-range vertical take-off and landing (VTOL) aircraft. The construction of the prototype required the development of a vectoring system for the tail propulsion system and a dedicated VTOL aircraft stabilization and control system. The prototype of the turbine drive system was based on energy-efficient electric motors. Research also involved modeling the turbine propulsion aerodynamics with usage of computational fluid dynamics (CFD) analysis. As a result of the design stage, a prototype of the propulsion system was created, on which flight tests were carried out. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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Nonlinear Modes as a Tool for Comparing the Mathematical Structure of Dynamic Models of Soft Robots

arXiv

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arXiv 2024年

作者： Pustina, Pietro Calzolari, Davide Albu-Schäffer, Alin De Luca, Alessandro Della Santina, Cosimo Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Institute of Robotics and Mechatronics German Aerospace Center Oberpfaffenhofen82234 Germany Department of Informatics Technical University of Munich Garching85748 Germany Department of Cognitive Robotics Delft University of Technology Delft Netherlands

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characteristics of the theoretically infinite-dimensional dynamics. However, there is no quantitative way to measure if the ROM of a soft robot has succeeded in this task. In other fields, like structural dynamics or flexible link robotics, linear normal modes are routinely used to this end. Yet, this theory is not applicable to soft robots due to their nonlinearities. In this work, we propose to use the recent nonlinear extension in modal theory –called eigenmanifolds– as a means to evaluate control-oriented models for soft robots and compare them. To achieve this, we propose three similarity metrics relying on the projection of the nonlinear modes of the system into a task space of interest. We use this approach to compare quantitatively, for the first time, ROMs of increasing order generated under the piecewise constant curvature (PCC) hypothesis with a high-dimensional finite element (FE)-like model of a soft arm. Results show that by increasing the order of the discretization, the eigenmanifolds of the PCC model converge to those of the FE model. © 2024, CC BY.

关键词： Structural dynamics

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Optimal Reorientation of Planar Floating Snake Robots with Collision Avoidance

SSRN

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SSRN 2023年

作者： Itani, Omar Shammas, Elie Jaoude, Dany Abou Vision and Robotics Lab Mechanical Engineering Department American University of Beirut Beirut Lebanon Control and Optimization Lab Mechanical Engineering Department American University of Beirut Beirut Lebanon

In this paper, a motion planning algorithm for floating planar under-actuated hyper-redundant snake robots is proposed. The presented algorithm generates locally optimal shape trajectories, i.e., continuous trajectories in the base space of the robot. Such shape trajectories produce a desired rotation of the snake robot, i.e., change in the uncontrolled orientation fiber variable. The proposed method formulates the motion planning problem as an optimization problem where the objective function could be defined to minimize various metrics, such as energy-based cost functions. Additionally, the proposed motion planning algorithm uses a heuristic to generate shape trajectories that avoid self-intersections and obstacle collision. Hence, the motion planning method generates shape trajectories that locally minimize user-defined cost functions and eliminate self-intersections or obstacle collision. The proposed gait generation method is validated using numerical simulations of five-link and seven-link snake robots. © 2023, The Authors. All rights reserved.

关键词： Trajectories

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