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Optimal Positioning Strategy for Multi-Camera Zooming Drones

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1802-1818页

作者： Manuel Vargas Carlos Vivas Teodoro Alamo Department of System Engineering and Automation University of SevilleSeville 41092Spain

In the context of multiple-target tracking and surveillance applications,this paper investigates the challenge of determining the optimal positioning of a single autonomous aerial vehicle or agent equipped with multiple independently-steerable zooming cameras to effectively monitor a set of targets of *** camera is dedicated to tracking a specific target or cluster of *** key innovation of this study,in comparison to existing approaches,lies in incorporating the zooming factor for the onboard cameras into the optimization *** enhancement offers greater flexibility during mission execution by allowing the autonomous agent to adjust the focal lengths of the onboard cameras,in exchange for varying real-world distances to the corresponding targets,thereby providing additional degrees of freedom to the optimization *** proposed optimization framework aims to strike a balance among various factors,including distance to the targets,verticality of viewpoints,and the required focal length for each *** primary focus of this paper is to establish the theoretical groundwork for addressing the non-convex nature of the optimization problem arising from these *** this end,we develop an original convex approximation *** paper also includes simulations of diverse scenarios,featuring varying numbers of onboard tracking cameras and target motion profiles,to validate the effectiveness of the proposed approach.

关键词： Convex optimization projective transformation unmanned aerial vehicle visual object tracking visual surveillance.

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Kernel-Based State-Space Kriging for Predictive Control

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1263-1275页

作者： A.Daniel Carnerero Daniel R.Ramirez Daniel Limon Teodoro Alamo the Department of System Engineering and Automation University of SevilleSevilla 41020Spain the School of Engineering Tokyo Institute of TechnologyTokyo 152-8552Japan Department of System Engineering and Automation University of SevilleSevilla 41020Spain

In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging(K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control(NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller.

关键词： Data-driven methods model identification Kernel methods predictive control

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Contribution of EEG Signals for Students’ Stress Detection

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IEEE Transactions on Affective Computing 2024年第2期16卷 1235-1246页

作者： Fernandez, Jonah Martínez, Raquel Innocenti, Bianca López, Beatriz The University of Girona Spain The Department of System Engineering and Automation University of the Basque Country Spain The Department of Electrical Electronics and Automation Engineering the University of Girona Spain

Stress is a prevalent global concern impacting individuals across various life aspects. This paper investigates stress detection using electroencephalographic (EEG) signals, which have proven valuable for studying neural correlates of stress. Stress was induced in students, and physiological data was recorded as part of the experimental setup. Different feature sets were extracted and four machine learning models, including LightGBM, Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), were utilized for classification tasks. The findings indicate that the mean and standard deviation of 19 channels consistently outperform other feature sets. LightGBM demonstrates superior performance across all scenarios compared to CNN, KNN, and SVM. Overall, this study presents an effective stress detection approach using EEG signals and demonstrates the potential of integrating simple statistical features for enhanced classification accuracy. The findings contribute to the advancement of stress monitoring technologies, with potential applications in wearables and BCIs for real-time stress management. © 2010-2012 IEEE.

关键词： Convolutional neural networks

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Controllability of heterogeneous networked sampled-data systems

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Science China(Information Sciences) 2025年第3期68卷 302-318页

作者： Zixuan YANG Lin WANG Xiaofan WANG Guanrong CHEN Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China School of Mechatronic Engineering and Automation Shanghai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Electrical Engineering City University of Hong Kong

This study investigates the controllability of a general heterogeneous networked sampled-data system(HNSS) consisting of nonidentical node systems, where the inner coupling between any pair of nodes can be described by a unique *** signals on control and transmission channels are sampled and held by zero-order holders, and the control sampling period of each node can be different. Necessary and sufficient controllability conditions are developed for the general HNSS, using the Smith normal form and matrix equations, respectively. The HNSS in specific topology or dynamic settings is discussed subsequently with easier-to-verify conditions derived. These heterogeneous factors have been determined to independently or jointly affect the controllability of networked sampled-data systems. Notably, heterogeneous sampling periods have the potential to enhance the overall controllability, but not for systems with some special dynamics. When the node dynamics are heterogeneous,the overall system can be controllable even if it is topologically uncontrollable. In addition, in several typical heterogeneous sampled-data multi-agent systems, pathological sampling of single-node systems will necessarily cause overall uncontrollability.

关键词： network controllability sampled-data system heterogeneous system inner coupling multi-agent system

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Fractional Order Iteration for Gradient Descent Method Based on Event-Triggered Mechanism

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Journal of systems Science & Complexity 2023年第5期36卷 1927-1948页

作者： LU Jiajie WANG Yong FAN Yuan Department of Automation University of Science and Technology of ChinaHefei 230026China Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China

In this work,a novel gradient descent method based on event-triggered strategy has been proposed,which involves integer and fractional order ***,the convergence of integer order iterative optimization method and the stability of its associated system with integrator dynamics are *** on this result,a fractional order iteration approach has been developed by modelling the system with fractional order ***,to reduce the comsumption of computation,a feedback based event-triggered mechanism has been introduced to the gradient descent *** convergence of this new event-triggered optimization algorithm is guaranteed by using a Lyapunov method,and Zeno behavior is proved to be avoided ***,the effectiveness and advantages of the proposed algorithms are verified by numerical simulations.

关键词： Event-triggered mechanism fractional order iteration gradient descent Zeno behavior

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Bolt-YOLO: Research on an Algorithm Framework for Detecting Bolt Defects in Transmission Lines

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IEEE Transactions on Power Delivery 2025年第3期40卷 1718-1729页

作者： He, Min Qin, Liang Deng, Xinlan Liu, Qing Liu, Kaipei Wuhan University Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy Department of Electrical Engineering and automation Wuhan430072 China

In transmission lines, bolt fittings are critical components that connect towers and insulators. These fittings are prone to defects, such as loosening or missing bolts, due to natural environmental factors, posing significant risks to the system's operation. Drone inspection images are increasingly used for the intelligent detection of bolt fittings, yet the small size of bolt components and the uneven distribution of sample quantities limit detection accuracy. To address these challenges, this paper proposes a multi-type bolt fitting defect detection algorithm aimed at improving detection accuracy and robustness. To tackle the issue of small target detection, we design a novel down-sampling method that incorporates both global and local feature perception, reducing the loss of small target information. This method is integrated into the Res-PANet, a multi-scale feature fusion structure based on residual skip connections, which compensates for the loss of small target details in high-level semantic features. Additionally, to address the sample imbalance problem, we introduce adaptive class weighting and a Minimum Peripheral Point Distance (MPPD) bounding box similarity constraint to better locate imbalanced sample regions from the loss function perspective. Experimental results, using a detection dataset containing four types of targets—normal bolts, missing pins, loose pins, and missing nuts—demonstrate the effectiveness of the proposed algorithm. Compared to the original YOLOv8 model, the improved model shows a 1.9% accuracy improvement for detecting missing pins, a 3.2% improvement for loose pins, and a 4.2% improvement for missing nuts. The overall mean accuracy increased from 69.5% to 72.1%. These results provide a solid technical foundation for detecting bolt fitting defects in transmission lines, offering significant practical value for future inspection tasks. © 1986-2012 IEEE.

关键词： Bolts

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A Survey on Collision Avoidance for Multi-robot systems

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Journal of Institute of Control, Robotics and systems 2024年第4期30卷 402-411页

作者： Park, Jungwon Oh, Dahyun Kim, H. Jin Department of Mechanical System Design Engineering Seoul National University of Science and Technology Korea Republic of Department of Aerospace Engineering and Automation and Systems Research Institute Seoul National University Korea Republic of

Multi-robot systems (MRS) enable cooperation between multiple robots to achieve common goals or tasks. These systems can enhance efficiency and productivity in various applications, such as transportation, manufacturing, and exploration. However, a critical issue in MRS operation is the possibility of collisions between robots or with static/dynamic obstacles. This survey presents the latest trends and advancements in collision avoidance approaches for multi-robot systems. We analyze centralized and distributed collision avoidance methods, examining the overall performance, applicable vehicle platforms, and the necessity for inter-robot communication. This survey also explores the applicability of reinforcement learning-based methods for collision avoidance in multi-agent systems. © ICROS 2024.

关键词： Multipurpose robots

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Physical Mechanism Enabled Neural Network for Power system Dynamic Security Assessment

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CSEE Journal of Power and Energy systems 2024年第6期10卷 2296-2307页

作者： Guozheng Wang Jianbo Guo Shicong Ma Kui Luo Xi Zhang Qinglai Guo Shixiong Fan Tiezhu Wang Weilin Hou Department of Power System China Electric Power Research InstituteBeijing 100192China School of Automation Beijing Institute of TechnologyBeijing 100081China Department of Electrical Engineering Tsinghua UniversityBeijing 100084China

Data-driven artificial intelligence technologies have emerged as increasingly fascinating tools for assessing power system ***,their inherent mechanism of inexplicability and unreliability now limits their scalability in power *** address this problem,this paper proposes a neural network design method empowered by physical mechanisms for power system security *** incorporates geometric characteristics of dynamic security regions into the network training process and constructs connections between network structure and system's unstable mode,which can perform security assessment with a neural network efficiently while ensuring physical ***,a credibility evaluation mechanism is established to ensure credibility of neural network predictions and reduce ***,effectiveness of the proposed method is verified on test *** and considerations in building a neural network with interpretable structures and credible predictions can provide a reference for machine intelligence applied in other industrial systems.

关键词： Credibility index machine intelligence neural network structure physical properties power system security assessment

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Development of a cable-driven redundant space manipulator with large bending angle by combining quaternion joints and segmented coupled linkages mechanism

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Chinese Journal of Aeronautics 2023年第11期36卷 483-499页

作者： Taiwei YANG Jian HUANG Wenfu XU Ke SHAO Bin LIANG School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China Department of Automation Tsinghua UniversityBeijing 100854China

A cable-driven redundant manipulator has significant potential in confined space applications, such as environmental exploration, equipment monitoring, or maintenance. A traditional design requires 3N driving motors/cables to supply 2N degrees of freedom(DOF) movement *** number of motors is 1.5 times that of the joints’ DOF, increasing the hardware cost and the complexity of the kinematics, dynamics, and control. This study develops a novel redundant space manipulator with decoupled cable-driven joints and segmented linkages. It is a 1680 mm continuum manipulator with eight DOF, consisting of four segmented linkages driven by eight motors/pairs of cables. Each segment has two equivalent DOF, which are realized by four quaternion joints synchronously driven by two linkage cables. The linkage cables of adjacent joints are symmetrically decoupled and offset at 180°. This design allows equal-angle movement of all the joints of each segment. Moreover, each decoupling driving mechanism is designed based on a pulley block composed of two fixed and movable pulleys. The two movable pulleys realize the opposite but equidistant motions of the two driving cables, i.e., pulling and loosening, assuring symmetrical movements of the two driving cables of each segment. Consequently, the equivalent 2N-DOF joints are driven only by 2N motors, significantly reducing the hardware cost and simplifying the mapping relationship between the motor angle/cable length and the joint angle. Furthermore, the bending range of each segment could reach 360°, which is three times that of a traditional design. Finally, a prototype has been developed and experimented with to verify the performance of the proposed mechanism and the corresponding algorithms.

关键词： Cable-driven Mechanism design Redundant manipulators Segmented linkages Kinematics

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