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Federated management: Toward Federated Services and Federated Security in Federated Ecology

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第6期8卷 1283-1290页

作者： Wang, Fei-Yue Qin, Rui Li, Juanjuan Wang, Xiao Qi, Hongwei Jia, Xiaofeng Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Datatang Beijing Technol Co Ltd Beijing 100192 Peoples R China Beijing Big Data Ctr Data Management Dept Beijing 100101 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Gansu 730000 Peoples R China

Welcome to the last issue of IEEE Transactions on Computational Social systems (IEEE TCSS) in 2021. For IEEE TCSS, 2021 is an exciting year. TCSS has been added to the ISI Web of Science Sources Citation Index Expanded (SCIE) database in 2021, and all articles published since 2018 have been indexed by SCIE. This is an important milestone in the development of TCSS. We would like to take this opportunity to thank and congratulate everyone for their great efforts and supports. We are looking forward to working together to further improve the publication quality and speed up the review process of TCSS in the upcoming 2022.

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Continuous-Time Linear Parallel Output Regulation

Continuous-Time Linear Parallel Output Regulation

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Chinese Automation Congress (CAC)

作者： Hongyang Li Qinglai Wei Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665426473

ISBN: (纸本)9781665426480

A novel parallel output regulation method is presented in this paper. The framework of the parallel output regulation problem is first introduced. Then, the existence of parallel controller is analyzed detailly in the situation that only the measurable information is obtained. Finally, the simulation example is given to show the performance of the presented method.

关键词： Automation control systems Regulation

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A structured light vision sensor for on-line weld bead measurement and weld quality inspection

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第5-6期106卷 2065-2078页

作者： Han, Yanqing Fan, Junfeng Yang, Xiaozhong North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Weld bead measurement and weld quality inspection are important parts in industrial welding. In this paper, a structured light vision sensor is developed to achieve on-line weld bead measurement and weld quality inspection. Firstly, a structured light vision sensor with a narrow-band optical filter is developed to reduce welding noises such as arc lights and splashes. Secondly, the weld bead type identification algorithm including image pre-processing, baseline extraction, and weld bead classification is proposed to classify filling weld bead and capping weld bead. Thirdly, feature extraction algorithms of filling weld bead and capping weld bead are presented to obtain corresponding feature points. Combining the image coordinates of feature points with structured light vision model, the weld bead size could be obtained and the weld quality could be evaluated. Finally, many weld bead measurement and weld quality inspection experiments are conducted. Experimental results demonstrate that the developed structured light vision sensor and proposed methods could achieve satisfactory performance for weld quality inspection.

关键词： Weld bead measurement Weld quality inspection Structured light vision Image processing 3D reconstruction

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Prediction model-based learning adaptive control for underwater grasping of a soft manipulator

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2021年第3期5卷 337-353页

作者： Yang, Hui Liu, Jiaqi Fang, Xi Chen, Xingyu Gong, Zheyuan Wang, Shiqiang Kong, Shihan Yu, Junzhi Wen, Li Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Peking Univ Beijing Innovat Ctr Engn Sci & Adv Technol Beijing Peoples R China

Soft robotic manipulators have promising features for performing non-destructive underwater tasks. Nevertheless, soft robotic systems are sensitive to the inherent nonlinearity of soft materials, the underwater flow current disturbance, payload, etc. In this paper, we propose a prediction model-based guided reinforcement learning adaptive controller (GRLMAC) for a soft manipulator to perform spatial underwater grasping tasks. In the GRLMAC, a feed-forward prediction model (FPM) is established for describing the length/pressure hysteresis of a chamber in the soft manipulator. Then, the online adjustment for FPM is achieved by reinforcement learning. Introducing the human experience into the reinforcement learning method, we can choose an appropriate adjustment action for the FPM from the action space without the offline training phase, allowing online adjusting the inflation pressure. To demonstrate the effectiveness of the controller, we tested the soft manipulator in the pumped flow current and different gripping loads. The results show that GRLMAC acquires promising accuracy, robustness, and adaptivity. We envision that the soft manipulator with online learning would endow future underwater robotic manipulation under natural turbulent conditions.

关键词： Non-destructive underwater tasks Guided reinforcement learning Prediction model Soft manipulator Underwater environment

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Single Network Robust Adaptive Critic-Based Trajectory Tracking control for Wheeled Mobile Robot with Wheel Sliding 6

Single Network Robust Adaptive Critic-Based Trajectory Track...

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6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Wang, Hongze Zhang, Jinxin Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China

ISBN: (纸本)9780738133645

In the trajectory tracking control problem of wheeled mobile robot with wheel sliding, the design process of traditional method is divided into two steps. This requires two controllers, which makes debugging difficult and error large. We propose one-step single network (OSSN), a method that combines backstepping and robust adaptive critic. Specifically, we use the backstepping with designed auxiliary variables to integrate the pose error and velocity error into a unified affine nonlinear system with disturbance term, which is convenient for tracking pose and velocity simultaneously. Then, the reward function including error, energy and disturbance information is specially designed. By solving the Hamilton-Jacobi-Bellman (HJB) equation, the optimal control is calculated. A Lyapunov function is designed to prove that under the effect of this special reward function, the optimal control can achieve the asymptotic stability of tracking errors. Besides, in order to reduce the training time, different from the traditional Actor-Critic structure, this paper uses the single network robust adaptive critic to approximate the optimal cost function from which the optimal control torques can be deduced. The simulation results show that this OSSN algorithm can realize good trajectory tracking performance of the wheeled mobile robot with wheel sliding.

关键词： Training Asymptotic stability Adaptive systems Backstepping Trajectory tracking Simulation Wheels

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Fully Robotized 3D Ultrasound Image Acquisition for Artery

Fully Robotized 3D Ultrasound Image Acquisition for Artery

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chen, Mingcong Huang, Yuanrui Chen, Jian Zhou, Tongxi Chen, Jiuan Liu, Hongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Kings Coll London Sch Biomed Engn & Imaging Sci London SE1 7EU England

ISBN: (纸本)9798350323658

Current imaging of the artery relies primarily on computed tomography angiography (CTA), which requires contrast injections and exposure to radiation. In this paper, we present a method for fully autonomous artery 3D image acquisition using a linear ultrasound (US) probe and a 6 DoFs robot arm with a 3D camera. Robotic vessel acquisition can minimize tissue deformation and permit the reproduction of scans. Additionally, the robotic-based acquisition can provide more precise vessel position data that can be utilized for 3D reconstruction as a preoperative image. The first scanning point is determined by the 3D camera using a neural network for leg area estimation. A visual servo algorithm adjusts the in-plane motions using a cross-sectional vessel segmentation produced by a neural network with a UNet structure, while a US confidence map regulates the in-plane rotation. The robot is equipped with impedance control to maintain a constant and safe scan. Experiments on a leg phantom and a volunteer indicate that the robot can follow the vessel and modify its position to provide a sharper US image. The average error of phantom scanning in y-axis and z-axis are 0.2536mm and 0.2928mm, respectively, while the root means square error (RMSE) of contact force in the volunteer experiment is 0.2664N. In addition, a 3D vessel reconstruction demonstrates the possibility of robotic US acquisition as a preoperative image.

关键词： Phantoms

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Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG

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COGNITIVE NEURODYNAMICS 2021年第1期15卷 181-189页

作者： Fan, Chen-Chen Yang, Hongjun Hou, Zeng-Guang Ni, Zhen-Liang Chen, Sheng Fang, Zhijie Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

Deep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet. It contains a newly designed attention module: 3D-AM, which is used to learn the attention weights of EEG channels, time points, and feature maps. It provides a way to automatically learn the electrode and time selection. QNet uses a dual branch structure to fuse bilinear vectors for classification. It performs four, three, and two classes on the EEG Motor Movement/Imagery Dataset. The average cross-validation accuracy of 65.82%, 74.75%, and 82.88% was obtained, which are 7.24%, 4.93%, and 2.45% outperforms than the state-of-the-art, respectively. The article also visualizes the attention weights learned by QNet and shows its possible application for electrode channel selection.

关键词： EEG Motor imagery Convolutional neural network Bilinear vectors Attention mechanism

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

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arXiv 2024年

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

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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SADRL: Merging human experience with machine intelligence via supervised assisted deep reinforcement learning

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NEUROCOMPUTING 2022年 467卷 300-309页

作者： Li, Xiaoshuang Wang, Xiao Zheng, Xinhu Jin, Junchen Huang, Yanhao Zhang, Jun Jason Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266109 Peoples R China Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA PCITECH PCI Intelligent Bldg2 Xincen Fourth Rd Guangzhou 510653 Peoples R China China Elect Power Res Inst State Key Lab Power Grid Safety & Energy Conserva Beijing 100192 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

Deep Reinforcement Learning (DRL) has proven its capability to learn optimal policies in decision-making problems by directly interacting with environments. Meanwhile, supervised learning methods also show great capability of learning from data. However, how to combine DRL with supervised learning and leverage additional knowledge and data to assist the DRL agent remains difficult. This study proposes a novel Supervised Assisted Deep Reinforcement Learning (SADRL) framework integrating deep Q-learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC). Specifically, the proposed DQfDDBC method leverages historical demonstrations to pre-train a behavioral cloning model and consistently update it by learning the dynamically updated demonstrations. A supervised expert loss function is designed to compare actions generated by the DRL model with those obtained from the BC model to provide advantageous guidance for policy improvements. Experimental results in several OpenAI Gym environments show that the proposed approach accelerates the learning processes, and meanwhile, adapts to different performance levels of demonstrations. As illustrated in an ablation study, the dynamic demonstration and expert loss mechanisms using a BC model contribute to improving the learning convergence performance compared with the baseline models. We believe that SADRL provides an elegant framework and the proposed method can promote the integration of human experience and machine intelligence. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Deep reinforcement learning Behavioral cloning Dynamic demonstration Double DQN

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TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

arXiv

引用

arXiv 2024年

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

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

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