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International Conference on Intelligent Transportation

作者： Zhenhao Ye Yanlong Li Zhichao Cui Yuehu Liu Li Li Le Wang Chi Zhang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China School of Software Engineering Xi'an Jiaotong University Xi'an Shaanxi China School of Electronics and Control Engineering Chang'an University Department of Automation BNRist Tsinghua University Beijing China

Anomaly detection in surveillance videos is a crucial and challenging task in the intelligent transportation systems. Previous methods utilize a memory module to store prototypical feature embeddings as normal patterns learned from normal training data. However, due to the complexity of real-world scenarios, it is difficult to choose an appropriate size of memory module which can not only memorize normal patterns comprehensively, but also capable of dealing with unseen normal scenarios. To tackle this problem, we learn normal video patterns by constructing and exploring correlations between visual semantics. In the training stage, we act the self-attention map between embeddings as a description of information association between different visual semantics. A self-attention based feature aggregating module is designed to regenerate a feature map through aggregating embeddings with similar information based on the attention map. By decoding the generated feature map instead of the original one to predict the future frame of the input video clip, the model learns to build strong information association between normal visual semantics. Moreover, we observe that abnormal embeddings hardly build strong association with others. Thus, we design a feature-level anomaly criterion referred as prior deviation to increase the difference between attention maps generated by normal and abnormal frames. In the inferring stage, the proposed prior deviation jointly detects anomalies with pixel-level frame prediction error. Experiment results and ablation studies on mainstream benchmarks demonstrate the effectiveness of our design.

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Research on synergy based on correlation analysis of weighted samples of grasping action

Research on synergy based on correlation analysis of weighte...

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Chinese Automation Congress (CAC)

作者： Jianjun Yu Zihao Zhang Guoyu Zuo Gengyuan Liu Xuchen Li Beijing Key Laboratory of Computational Intelligence and Intelligent System College of Electronic and Control Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781665426480

The main research goal of underactuated manipulator is to reduce the number of controllers and improve the bionic ability. In this kind of research, underactuated hand design synergy is the mainstream research direction in recent years. Human hand synergy space is a parameterized representation of human hand motion based on a large number of human hand motion data. In the past synergy meta-analysis, samples were selected only based on grasping classification without considering people's daily habits, which led to the lack of bionic ability. In this paper, based on the most authoritative 33 kinds of grabbing classification, the samples are weighted and reconstructed according to the daily use habits; Principal component analysis is used to analyze the reconstructed samples, and the correlation between joints is calculated to obtain the synergy of human hand. The comparative experiments show that the underactuated transmission scheme based on the synergy design of reconstructed samples has higher similarity and stronger bionic ability than the ordinary sample scheme in most grasping scenes. On this basis, this paper studies the following ability between fingers through the calculation of correlation coefficient and clustering method, so as to further reduce the number of controllers.

关键词： Correlation coefficient Actuators Correlation Automation Clustering methods Grasping Aerospace electronics

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A one-step pseudolinear kalman filter for invasive target tracking in three-dimensional space

A one-step pseudolinear kalman filter for invasive target tr...

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

作者： Zhang, Ying Liang, Rui Xu, Sheng Zhang, Liyuan Zhang, Ye Xiao, Dandan State Grid Tianjin Electric Power Company City West Power Supply Branch Tianjin300190 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

This work studies the characteristics of the 3-dimensional (3D) pseudolinear Kalman filter (PLKF) proposed in the previous work [1] for angle-of-arrival (AOA) target tracking in the 3D space. The previous two-step 3D PLKF contains an xy-PLKF and a z-PLKF. This paper develops a one-step PLKF algorithm, which has low computational complexity. The estimation performance analysis of these two different PLKFs is provided. The one-step PLKF sacrifices the estimation accuracy to acquire a low computational complexity. In the distributed target tracking problem, the one-step PLKF can save a lot of communication costs compared with the two-step one. Furthermore, the stability of two PLKFs are also been simply proven. Simulation examples verify the effectiveness of the proposed one-step 3D PLKF to track an invasive target. Besides, the difference between the two PLKFs based on the theoretical analysis is also demonstrated by the simulation examples. © 2021 IEEE.

关键词： Target tracking

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Dynamic modeling and vibration analysis of a 6-DOFs industrial robot considering joint flexibility

Dynamic modeling and vibration analysis of a 6-DOFs industri...

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

作者： Shen, Guodong Xu, Sheng Chen, Chunjie Wang, Qiang Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

For the industrial robots, the vibration caused by the joint flexibility could affect the manipulator's performance significantly. Therefore, the modeling and vibration analysis of industrial robot considering joint flexibility has become an important topic in recent years. However, the research on flexible-joint manipulator is usually performed for planar robots, whereas the research on general spatial multi degrees of freedom (DOFs) industrial robots are still limited. In this paper, the dynamic modeling approach is developed for vibration analysis of the serial industrial robot having six DOFs. Firstly, based on the Spong's model of joint deformation, the flexibility of all joints is considered concurrently, and the partial velocity relationship between each link and the joint are deduced. Then, the dynamic model of the 6-DOFs flexible robot is derived according to the Kane equation. The results using the proposed Kane-based method are verified by several numerical simulations. Finally, vibration responses of the end-effector under different joint stiffness parameters are analyzed. The results in this study can offer some useful guidance for the path planning and control to suppress the vibration of the multi-DOFs industrial robots. © 2021 IEEE.

关键词： Vibration analysis

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A data-driven shared control system for exoskeleton rehabilitation robot

A data-driven shared control system for exoskeleton rehabili...

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

作者： Li, Feng He, Yong Li, Jinke Ni, Jiangpeng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

It is very important to establish a control operating system for rehabilitation robots, especially to improve the rehabilitation effect and human-machine interaction ability of patients. Exoskeletons have been proved to be effective in providing highly repeatable and accurate rehabilitation exercises, but most existing exoskeletons have inconsistent operating systems and data loss. This paper designed a novel data-driven shared control system(DDSCS) farmework, applied to different exoskeleton rehabilitation robots (ERR). Due to the unique physical characteristics of exoskeleton rehabilitation robots, it can't be adapt to different patients. Firstly, the DDSCS framework is established via the data-driven and shared technology, and the feasibility is analyzed. Secondly, to iterate the individualized gait trajectory, data-driven gait trajectory correction model is designed. Finally, the shared human-machine interface is developed, and the superiority and effectiveness of DDSCS framework are verified by the exoskeleton robot experiments. © 2021 IEEE.

关键词： control systems

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Muscle-Synergy-Based Planning and Neural-Adaptive control for a Prosthetic Arm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2021年第5期2卷 424-436页

作者： Li, Guoxin Li, Zhijun Li, Junjun Liu, Yueyue Qiao, Hong The Department of Automation University of Science and Technology of China Hefei230026 China The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The School of Automation Science and Engineering South China University of Technology Guangzhou511442 China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Institute of Neuroscience Chinese Academy of Science Shanghai200031 China

Upper limb loss has significant effects on the individual's quality of life. Artificial prosthetic limbs as an alternative to the lost limb are designed to allow amputees to regain motor function. Motion classification via extracted surface electromyogram (sEMG) signals is widely utilized to realize a friendly human-robot interface in the control of the prosthesis. However, limited classification of discrete motion patterns from sEMG prevents intuitive motor control. Thus, instead of using discrete patterns, decoding the human intention continuously from sEMG would significantly benefit the prosthesis control. In this article, we propose a muscle-synergy-based intention decoding and motion planning that can model a broad set of complex upper limb movements as a combination of motor primitives. A novel muscle activation-to-force mapping model is developed to detect muscular effort of the healthy side to drive the affected side. A neural-network-approximation-based controller is developed for the bionic neuroprosthetic arm to execute the movement. Operational experiments with prosthetic movement control were performed on four healthy participants and an upper limb amputee participant. Results of controlling prosthetic arm (0.94 ± 0.02 of R2 in the horizontal reaching tasks and 0.95 ± 0.01 in the vertical reaching tasks for the healthy subjects, 0.95 and 0.97 for the amputee) demonstrate that our control method could successfully capture human movement intention and effectively control the movement of prosthesis. Impact Statement-Powered robotic prostheses have enabled individuals with limb amputations to regain motor function via motion intention decoding from extracting surface electromyogram signals. Most research works have provided human-robot interaction through motion classification obtained from pattern recognition using surface electromyogram signals and simple movement control. However, decoding the human intent continuously from surface electromyogram is con

关键词： Matrix algebra

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A framework of cooperative UAV-UGV system for target tracking

A framework of cooperative UAV-UGV system for target trackin...

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

作者： Su, Zhilong Wang, Can Wu, Xinyu Dong, Yao Ni, Jiangpeng He, Bailin Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Beijing100049 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China

ISBN: (纸本)9781665436786

This paper proposed a framework of cooperative UAV-UGV system. This system consists of three modules: UAV, UGV, and ground station console. It has the characteristics of high cohesion, low coupling, cross platform, and cross language. Compared with the prior methods which UAV-UGV cooperation system that only uses UAV to track UGV, our method is more effective and can give full play to the advantages of UAV and UGV. We use UAV visual tracking algorithm and coordinate transformation to get the target location, and use UGV to navigate to the target location to keep up with the target. The high mobility of UAV makes the target tracking range of the whole system larger, the robustness of UGV is good, and the tasks can be performed more precisely. © 2021 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Accurate MTPA control Based on Ferrari Method for IPMSM with Partial Derivative Term Calculation

Accurate MTPA Control Based on Ferrari Method for IPMSM with...

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International Conference on Electrical machines and systems (ICEMS)

作者： Yiyang Li Xin Gu Zhiqiang Wang Liyan Guo Wei Chen Xinmin Li Chen Li School of Electronics and Information Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin China School of Electrical Engineering Tiangong University Tianjin China National Local Joint Engineering Research Center of Electrical Machine System Design and Manufacturing China Zhejiang University Advanced Electrical Equipment Innovation Center Hangzhou China

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

Even if the exact motor parameters are known, the MTPA strategy cannot obtain precise maximum torque per ampere (MTPA) points under large current amplitude because the partial derivative of inductance to current is ignored in the modeling process. In view of this, the MTPA mathematical model considering the partial derivative of inductance to current is established in this paper, and the MTPA point tracking error caused by ignoring the partial derivative in the modeling process based on Ferrari method is analyzed. Then, an offline-online combined method for calculating the partial derivative term is proposed to compensate the MTPA tracking error. On this basis, an MTPA control strategy based on Ferrari method considering the partial derivative of inductance to current is proposed. Experiment to verify the feasibility and effectiveness of the proposed method.

关键词： Inductance Analytical models Torque Accuracy Permanent magnet motors Mathematical models Trajectory

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RLSAC: Reinforcement Learning enhanced Sample Consensus for End-to-End Robust Estimation

arXiv

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arXiv 2023年

作者： Nie, Chang Wang, Guangming Liu, Zhe Cavalli, Luca Pollefeys, Marc Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Department of Computer Science ETH Zürich Switzerland Microsoft Mixed Reality AI Zürich Lab Switzerland

Robust estimation is a crucial and still challenging task, which involves estimating model parameters in noisy environments. Although conventional sampling consensus-based algorithms sample several times to achieve robustness, these algorithms cannot use data features and historical information effectively. In this paper, we propose RLSAC, a novel Reinforcement Learning enhanced SAmple Consensus framework for end-to-end robust estimation. RLSAC employs a graph neural network to utilize both data and memory features to guide exploring directions for sampling the next minimum set. The feedback of downstream tasks serves as the reward for unsupervised training. Therefore, RLSAC can avoid differentiating to learn the features and the feedback of downstream tasks for end-to-end robust estimation. In addition, RLSAC integrates a state transition module that encodes both data and memory features. Our experimental results demonstrate that RLSAC can learn from features to gradually explore a better hypothesis. Through analysis, it is apparent that RLSAC can be easily transferred to other sampling consensus-based robust estimation tasks. To the best of our knowledge, RLSAC is also the first method that uses reinforcement learning to sample consensus for end-to-end robust estimation. We release our codes at https://***/IRMVLab/RLSAC. © 2023, CC BY.

关键词： Reinforcement learning

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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