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Chinese Automation Congress (CAC)

作者： Hengfei Chen Xianda Liu Chuan Sheng Hao Pan College of Information Engineering Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

Since the industrial Internet entered people's field of vision, industrial control network security has gradually received attention. Traditional network security measurement methods rely on the accuracy of situation sequences and historical situation values, and various factors in the network have a great influence on the results. However, compared with industrial control networks, the number and types of affected factors are less than those of the modern Internet. Therefore, based on the research on network security situational awareness, this paper proposes an industrial control network security measurement method model based on GRU-SVM. First, the data sets with threat type labels are classified, and then the similarity scoring mechanism and softmax function are introduced, to process the classification results. For the GRU-SVM classification module, the analysis of the simulation results shows that the convergence is better and the robustness is stronger. For the safety prediction measurement part, by comparing with different models, the mean absolute error and root mean square error are smaller and the reliability is higher.

关键词： Industrial control Simulation Measurement uncertainty Network security Predictive models Logic gates Robustness

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State-of-Charge (SOC) Estimation of Lithium-Ion Battery Based on Unscented Kalman Filtering

State-of-Charge (SOC) Estimation of Lithium-Ion Battery Base...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Mengfan Miao Ligang Li Zhongfeng Wang Jing Jiang College of Automation and Electrical Engineering Shenyang Ligong University Liaoning China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Real-time estimation of State of Charge (SOC) for lithium-ion batteries is one of the core technologies in Battery Management systems (BMS). Accurate SOC estimation is fundamental for managing lithium-ion batteries. In this study, a second-order RC equivalent circuit model is established, and parameter identification methods are employed to determine the model parameters. The identified parameters are then incorporated back into the second-order RC model. The accuracy of these parameters and the model is validated through Hybrid Pulse Power Characteristic (HPPC) tests. Finally, the Unscented Kalman Filtering algorithm (UKF) is utilized to estimate battery SOC. Experimental results demonstrate that SOC estimation based on UKF exhibits high precision and stability.

关键词： Lithium-ion batteries Accuracy Parameter estimation Simulation Estimation Data models Stability analysis

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SLAM-Based Joint Calibration of Differential RSS Sensor Array and Source Localization

SLAM-Based Joint Calibration of Differential RSS Sensor Arra...

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Annual Conference of Industrial Electronics Society

作者： Linya Fu Xu Qiao Shoudong Huang Guoqiang Mao Zhiyun Lin Youfu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern Uni. of Sci. and Tech. Shenzhen China Robotics Institute Uni. of Technology Sydney Sydney Australia State Key Laboratory of Integrated Services Networks Xidian University China Department of Mechanical Engineering City University of Hong Kong China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern Uni. of Sci. and Tech. Shenzhen China

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://***/SUSTech2022/DRSS-sensor-array-calibration.

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Generating Datasets for Real-Time Scheduling on 5g New Radio

SSRN

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SSRN 2022年

作者： Jin, Xi Chai, Haoxuan Xia, Changqing Xu, Chi Zeng, Peng Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

5G is an advanced solution for industrial wireless motion control. However, since the scheduling model of 5G new radio (NR) is more complicated than those of other wireless networks, existing real-time scheduling algorithms cannot be used to improve the 5G performance. This results in NR resources not being fully available for industrial systems. Supervised learning has been widely used to solve complicated problems, and its advantages have been demonstrated in multiprocessor scheduling. One of the main reasons why supervised learning has not been used for 5G NR scheduling is the lack of training datasets. Therefore, in this paper, we propose two methods based on optimization modulo theories (OMT) and satisfiability modulo theories (SMT) to generate training datasets for 5G NR scheduling. Our OMT-based method contains fewer variables than existing work so that the Z3 solver can find optimal solutions quickly. To further reduce the solution time, we transform the OMT-based method into an SMT-based method and tighten the search space of SMT based on three theorems and an algorithm. Finally, we conduct extensive evaluations. The evaluation results indicate that our SMT-based method reduces the solution time by 74.7% compared to existing ones. © 2022, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Fault Diagnostic Opportunities for Electromagnetic Coils of Active Magnetic Bearings using Physics-of-Failure Analysis

Fault Diagnostic Opportunities for Electromagnetic Coils of ...

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System Reliability and Safety Engineering (SRSE), International Conference on

作者： Ruiqi Li Kai Wang Peng Zeng Jiwei Cao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Department of Electrical Engineering Harbin Institute of Technology China

Active magnetic bearings (AMBs) have many advantages over traditional oil bearings due to their non-contact characteristics. They are environmentally friendly solution and have been proven to be highly reliable and available. Electromagnetic coils are vital components of AMB and they are failure prone. In this paper, the failure mode of magnetic bearing coil and its corresponding failure mechanism are reviewed by FMMEA method, and the existing coil fault diagnosis methods are introduced. Future directions for coil insulation health monitoring aiming at the application characteristics of AMBs are also discussed.

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Cloud-aided state estimation of a full-car semi-active suspension system

arXiv

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arXiv 2017年

作者： Zhang, Lixian Yin, Xunyuan Shen, Junnan Yu, Haitao Research Institute of Intelligent Control and System Harbin Institute of Technology Harbin150080 China Department of Chemical and Materials Engineering University of Alberta EdmontonT6G 2V2 Canada State Key Laboratory of Robotics and Systems Harbin Institute of Technology 150001 China

In this work, we investigate a state estimation problem for a full-car semi-active suspension system. To account for the complex calculation and optimization problems, a vehicle-to-cloud-to-vehicle (V2C2V) scheme is utilized. Moving horizon estimation is introduced for the state estimation system design. All the optimization problems are solved in a remotely-embedded agent with high computational ability. Measurements and state estimates are transmitted between the vehicle and the remote agent via networked communication channels. The effectiveness of the proposed method is illustrated via a set of simulations. Copyright © 2017, The Authors. All rights reserved.

关键词： State estimation

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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A biofuel risk management model for biomass-based heat sources considering supply-demand-side uncertainties

A biofuel risk management model for biomass-based heat sourc...

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12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)

作者： D. Fu T. Zhou T. Yang T. Zhang J. Dong Key Laboratory of Networked Control Systems Institutes for Robotics and Intelligent Manufacturing Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang People's Republic of China Technical Management Division Guodian Technology & Environment Group Co Ltd. Beijing People's Republic of China Digital Factory Department Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang People's Republic of China

Biofuel-based heat sources are gradually penetrated into heating systems in Northern China in the background of carbon neutrality. It is essential to conduct biofuel risk management study, not only for enhancing the ability of continuous heat supply for heating systems, but also improving the ability of effective cost control. A biofuel risk management model is thus proposed innovatively for biomass-based heat sources considering supply-demand-side uncertainties. The developed model integrates Chance-constrained programming, Two-stage stochastic programming and the traditional linear programming into a general framework. After that, the developed model is applied in to a real heat-source case of biofuel risk management under uncertainties and the corresponding solutions on biofuel risk management are generated. The beneficial results of biofuel allocation pattern can be obtained for decision-makers to provide the fuel management support under different risk levels. Besides, a trade-off analysis between the constraint-violation risk and the system cost can also be provided to conduct in-depth analysis.

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Hierarchical Task Impedance control of a Serial Manipulator for Minimally Invasive Surgery

Hierarchical Task Impedance Control of a Serial Manipulator ...

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IEEE International Conference on Human-Machine systems

作者： Hang Su Chenguang Yang Jiehao Li Yiming Jiang Giancarlo Ferrigno Elena De Momi Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Bristol Robotics Laboratory University of the West of England Bristol United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Flexibility and robustness have become key points in the development of surgical robot controller for physical interactions. However, the conventional impedance control schemes unaware of the actual surgical scenario, including complex physical interaction on the robot arm, lead to the loss of accuracy. In this paper, a hierarchical task impedance control scheme is proposed for Minimally Invasive Surgery (MIS) based on an operational space formulation of a 7 DoFs redundant robot. Its redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a flexible workspace for the medical staff to assist physicians. In addition to the achievement of the classical whole-body impedance control, the issue of uncertain disturbances will be addressed by a decoupled adaptive approximation based on a radial basis function neural network (RBFNN) within the control framework. Task performances under the hierarchical task impedance controller were validated and compared with previous work in the literature. Experimental results showed its improved performance in terms of positional error and RCM constraint, regardless of the existing uncertain physical interaction.

关键词： Task analysis Impedance Manipulators Tools Surgery Jacobian matrices

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NeRF in robotics: A Survey

arXiv

引用

arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in robotics and The Advance of NeRF in robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： robotics

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