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2021 International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2021

作者： Li, Boyu Jin, Tao Zhu, Yuanheng Li, Haoran Wu, Yingnian Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Beijing Information Science and Technology University School of Automation Beijing100192 China

ISBN: (纸本)9781665402453

Robots are playing a more and more important role in people's production and life, recently. However, robot control in dynamic environment is still a difficulty. With the great breakthrough of deep reinforcement learning in the field of video games, this method is also extended to the field of robots. Due to the gap between the simulation environment and the real environment, the deep reinforcement learning algorithm trained in the simulation environment is difficult to be applied to the real environment. Aiming at the gimbal control with two degrees of freedom (DOF), a pipline combining system identification and deep reinforcement learning is proposed. On the one hand, the shooting accuracy of the gimbal to moving objects is improved through deep reinforcement learning algorithm. On the other hand, the gap between simulation and reality is reduced through system identification. The method is verified in the RoboMaster University AI Challenge (RMUA) shooting system. The results show that the shooting accuracy is better than the classical control method. © 2021 IEEE.

关键词： Deep learning

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A Study on Decentralized Autonomous Organizations Based Intelligent Transportation System enabled by Blockchain and Smart Contract

A Study on Decentralized Autonomous Organizations Based Inte...

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2021 China Automation Congress, CAC 2021

作者： Hou, JiaChen Ding, WenWen Liang, Xiaolong Zhu, FengHua Yuan, Yong Wang, FeiYue Institute of Systems Engineering Macau University of Science and Technology China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Mathematics Renmin University of China Beijing China

ISBN: (纸本)9781665426473

With the rapid development of the transportation industry, Intelligent Transportation System (ITS) tries to adapt to industry changes through constructing new organizational forms, management methods, and incentive mechanisms. However, many new governance issues have arisen due to the transportation system's complexity, diversity, and uncertainty. DAO(Decentralized autonomous organization) enabled by blockchain and smart contracts are regarded as effective methods to solve the organizational governance problems for complex systems. Therefore, this article analyzes the main issues currently faced by ITS. Then, this paper proposed a 6-layer DAO structure framework for ITS and discussed its key research issues. Finally, a case study for the Macau transportation system is carried out, and further research directions are proposed. The DAO-based transportation system will represent the future trend of ITS development. This paper aims to motivate more discussion for this new direction and provide helpful guidance and reference for future research work. © 2021 IEEE

关键词： Smart contract

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UCAS-hand: An underactuated powered hand exoskeleton for assisting grasping task

UCAS-hand: An underactuated powered hand exoskeleton for ass...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Li, Houcheng Cheng, Long Li, Zhengwei Li, Guotao Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665436786

This paper presents a novel underactuated coupled adaptive hand exoskeleton, called UCAS-Hand, which is designed to assist users with weak muscle strength to complete the operation of daily living items. In mechanical design, the proposed UCAS-Hand considers the human-robot kinematic compatibility, grasping adaptability for different objects, portability with motors attached to the hand, force transfer efficiency with applying normal force to the finger phalanges, and passive backdrivability with low gear reduction ratio motor. The UCAS-Hand can realize finger underactuated motions such as flexion/extension and thumb underactuated motions such as flexion/extension, abduction/adduction, and thumb opposability. To minimize the backlashes of bevel gear transmission, a spherical four-bar mechanism and a spherical seven-bar mechanism are designed for the index finger and thumb mechanisms, respectively. Finally, the experiment is implemented to reveal the characteristics of the UCAS-Hand. © 2021 IEEE.

关键词： Machine design

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MetaSensing in Metaverses: See There, Be There, and Know There

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IEEE INTELLIGENT systems 2022年第6期37卷 7-12页

作者： Liu, Yuhang Shen, Yu Guo, Chao Tian, Yonglin Wang, Xiao Zhu, Yin Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automation Beijing 100190 Peoples R China Anhui Univ Hefei 266114 Peoples R China North Automatic Control Technol Inst Taiyuan 030006 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau 999078 Peoples R China

The concept of metaverses has received extensive attention recently and cyber-physical-social systems (CPSS) is its academic foundation. In almost all the applications of metaverses, the sensing system is an essential part and intelligent sensing capacity must be provided. However, due to the insufficient consideration of human factors in most of the studies, digital twins' sensing in cyber-physical systems cannot achieve smart sensing in metaverses. For this reason, a novel framework for intelligent sensing in metaverses, MetaSensing, is proposed based on parallel intelligence in CPSS. Within the framework of MetaSensing, there are four states of sensing: physical sensing, descriptive sensing, predictive sensing, and prescriptive sensing. To protect sensors' data privacy in metaverses, DAO-based decentralized sensing is introduced as a mechanism of the operation and maintenance for smart sensing industries.

关键词： Industries Data privacy Data security Organizations Human factors Maintenance engineering Real-time systems

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Call for V&VIV: Verification and Validation of Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2022年第3期7卷 401-406页

作者： Fei-Yue Wang Javier Ibanez-Guzman Yisheng Lv State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Expert Autonomous Systems Artificial Intelligence Renault S.A. SIVALab CNRS Common Laboratory Chateau Renault France

Presents a summary of articles included in this issue of the publication.

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A Learning Method for Feature Correspondence with Outliers 26

A Learning Method for Feature Correspondence with Outliers

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Yang, Xu Zeng, Shao-Feng Han, Yu Lu, Yu-Chen Liu, Zhi-Yong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Feature correspondence is an important topic in many computer vision or robot vision tasks. Different from traditional optimization based matching method, in the last two years, researchers are finally able to solve the matching process in a learning manner. As a representative method, SuperGlue achieves superior performance in many real-world tasks, but it still has problems in dealing with outlier features. Targeting at the outlier problem, this paper improves SuperGlue by introducing a deep learning based feature correspondence method, which consists of the pruned attentional graph neural network and the improved matching layer for the outlier problem. Experiments on real world images validate the effectiveness of the proposed method.

关键词： Learning systems Deep learning Computer vision Robot vision systems Graph neural networks Pattern recognition Task analysis

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A Collaborative Robot Torque Prediction Method Based on CNN-TCN Model

A Collaborative Robot Torque Prediction Method Based on CNN-...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Lina Tong Decheng Cui Chen Wang Liang Peng School of Mechanical Electronic and Information Engineering China University of Mining and Technology Beijing Beijing China State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) Institute of Automation Chinese Academy of Science Beijing China

The traditional dynamical models show lower accuracy when predicting joint movement, and should be compensated. This paper proposed a model combined with the convolutional network(CNN) and temporal convolutional network(TCN) to compensate for the joint torque prediction values that are calculated from the sensing information. The experiments on the Cooperative Universal Robotic Assistant 6 DoF(CURA6) open dataset, including multi-load and multi-velocity, showed the prediction error can be reduced by 20% compared to other network models. Since there are many kinds of joint movement information, the input data form of the deep learning model should be improved. Thus, the kinetic linearization model is proposed to modify the input of sensing data. According to the different motion types of the CURA6 dataset, comparative experiments were taken, and the mean absolute error was less than 6.8%.

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Sparse online kernelized actor-critic Learning in reproducing kernel Hilbert space

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 23-58页

作者： Yang, Yongliang Zhu, Hufei Zhang, Qichao Zhao, Bo Li, Zhenning Wunsch, Donald C. Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Univ Macau State Key Lab Internet Things Smart City Taipa 59193 Macao Peoples R China Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65401 USA

In this paper, we develop a novel non-parametric online actor-critic reinforcement learning (RL) algorithm to solve optimal regulation problems for a class of continuous-time affine nonlinear dynamical systems. To deal with the value function approximation (VFA) with inherent nonlinear and unknown structure, a reproducing kernel Hilbert space (RKHS)-based kernelized method is designed through online sparsification, where the dictionary size is fixed and consists of updated elements. In addition, the linear independence check condition, i.e., an online criteria, is designed to determine whether the online data should be inserted into the dictionary. The RHKS-based kernelized VFA has a variable structure in accordance with the online data collection, which is different from classical parametric VFA methods with a fixed structure. Furthermore, we develop a sparse online kernelized actor-critic learning RL method to learn the unknown optimal value function and the optimal control policy in an adaptive fashion. The convergence of the presented kernelized actor-critic learning method to the optimum is provided. The boundedness of the closed-loop signals during the online learning phase can be guaranteed. Finally, a simulation example is conducted to demonstrate the effectiveness of the presented kernelized actor-critic learning algorithm.

关键词： Reproducing kernel Hilbert space Actor-critic learning Value function approximation Online sparsification Non-parametric learning

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Spatio-Temporal Causal Transformer for Multi-Grained Surgical Phase Recognition 44

Spatio-Temporal Causal Transformer for Multi-Grained Surgica...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Chen, Hua-Bin Li, Zhen Fu, Pan Ni, Zhen-Liang Bian, Gui-Bin 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 Beijing Informat Sci & Technol Univ Sch Automat Beijing 100101 Peoples R China

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Automatic surgical phase recognition plays a key role in surgical workflow analysis and overall optimization in clinical work. In the complicated surgical procedures, similar inter-class appearance and drastic variability in phase duration make this still a challenging task. In this paper, a spatio-temporal transformer is proposed for online surgical phase recognition with different granularity. To extract rich spatial information, a spatial transformer is used to model global spatial dependencies of each time index. To overcome the variability in phase duration, a temporal transformer captures the multi-scale temporal context of different time indexes with a dual pyramid pattern. Our method is thoroughly validated on the public Cholec80 dataset with 7 coarse-grained phases and the CATARACTS2020 dataset with 19 fine-grained phases, outperforming state-of-the-art approaches with 91.4% and 84.2% accuracy, taking only 24.5M parameters.

关键词： Surgery Color Transformers Biology Indexes Data mining Task analysis

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Parallel System-Based Predictive control for Traffic Signals in Large-Scale Road Networks 26

Parallel System-Based Predictive Control for Traffic Signals...

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IEEE 26th International Conference on Intelligent Transportation systems (ITSC)

作者： Dai, Xingyuan Zhang, Yan Tang, Yiqing Chen, Hongrui Sun, Jiaming Cong, Fei Lu, Yanan Lv, Yisheng Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China China Telecom Digital City Technol Co Ltd Xiongan 071700 Peoples R China China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China

ISBN: (纸本)9798350399462

This paper proposes a parallel system-based predictive control (PPC) method to address the problem of active traffic signal control in large-scale urban road networks. The method leverages simulated artificial transportation systems to infer the short-term future operating states of the real transportation system. During the inference process, an efficient predictive learning-based multi-agent reinforcement learning (RL) algorithm is employed to optimize the cooperative control policies. The optimized policies are then deployed to the real transportation system at fixed intervals to adapt to the real-time and dynamic traffic flow. Experimental results demonstrate that PPC outperforms traditional traffic control methods and some multi-agent RL benchmarks in large-scale road network control scenarios with nearly two hundred intersections, showcasing superior generalization capabilities.

关键词： Roads and streets

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