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7th International Conference on Electromechanical Control technology and Transportation, ICECTT 2022

作者： Zhang, Wenming Zhang, Yun Chen, Ming Zeng, Lei Gao, Sheng Jiang, Xufu Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing China Beijing Satellite Manufacturing Limited Company Beijing China

ISBN: (纸本)9781510656659

The ball screw is a commonly used transmission component. When applied to the spacecraft mechanism, it is necessary to comprehensively consider space environmental factors such as on-orbit high and low temperature, vacuum, atomic oxygen, electron irradiation, single particle, ultraviolet and infrared irradiation. In view of the long-life application requirements of ball screws for spacecraft mechanism, the factors affecting the life of the ball screws are analyzed. Two design schemes are proposed according to the lubrication method of the ball screws and the material of ball for a ball screws of a certain spacecraft mechanism. The design scheme of ceramic ball + raceway sputtering MoS2 lubrication has been confirmed by the test. The design can work for no less than 7000 times under the specified load, which meets the requirements of the flight model on-orbit. Through the ball screw long-life test, it can be seen that the load capacity and service life of the solid lubricated ball screw are significantly reduced. In the development process of the spacecraft mechanism ball screw, a special life test must be carried out to verify whether the ball screw can meet the on-orbit requirements. © 2022 SPIE

关键词： Layered semiconductors

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TGCN-P: A TCN-GCN Network With Weighted Graph Constructed by Pearson Correlation Coefficient for Human Motion Tracking

TGCN-P: A TCN-GCN Network With Weighted Graph Constructed by...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Li, Xiaoyu Wang, Jingnan Huang, Binhua Liu, Chengliang Liu, Yiwen Zhang, Yupo Yi, Zhengkun Wu, Xinyu Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch Shenzhen518055 China

ISBN: (纸本)9798350327182

Neural networks based on graph convolution are simple and effective network structures that have better performance in some fields compared to traditional convolutional networks. However, when graph convolution networks (GCN) are applied to other fields, how to transform data or features into graph edges is a challenge needed to be studied. In previous studies of non-Euclidean spatial data such as relationship studies, people achieve graph construction by indicating the connection or not of nodes with binary values. However, when graph network is applied to some multi-sensor data scenarios, especially human motion tracking tasks, binarization is obviously not the best solution for the weights of edges. In addition, relying on manual graph construction may ignore some cross-space sensor relationships, which makes the update information of nodes far away slower or unavailable. Therefore, we try to use Pearson correlation coefficient (PCC) as the estimation of node correlation, and construct a complete graph to enhance the connectivity of nodes, which achieve faster signal transmission at remote nodes. After combining it with the temporal convolutional network (TCN), we propose a new network structure TGCN-P. Experimentally, this approach is more effective compared to other baseline models. © 2023 IEEE.

关键词： Convolution

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Reversible solid-liquid conversion enabled by self-capture effect for stable non-flow zinc-bromine batteries

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Green Energy & Environment 2024年第6期9卷 1035-1044页

作者： Xixi Zhang Xiaoke Wang Guangmeng Qu Tairan Wang Xiliang Zhao Jun Fan Cuiping Han Xijin Xu Chunyi Zhi Hongfei Li School of Physics and Technology University of JinanShandong250022China Songshan Lake Materials Laboratory DongguanGuangdong523808China Department of Materials Science and Engineering City University of Hong Kong83 Tat Chee AvenueHong KongChina Faculty of Materials Science and Engineering and Low Dimensional Energy Materials Research Center Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenGuangdong518055China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhenGuangdong518055China

Non-flow aqueous zinc-bromine batteries without auxiliary components(e.g.,pumps,pipes,storage tanks)and ion-selective membranes represent a cost-effective and promising technology for large-scale energy ***,they generally suffer from serious diffusion and shuttle of polybromide(Br^(-),Br^(3-))due to the weak physical adsorption between soluble polybromide and host carbon materials,which results in low energy efficiency and poor cycling ***,we develop a novel self-capture organic bromine material(1,10-bis[3-(trimethylammonio)propyl]-4,4'-bipyridinium bromine,NVBr4)to successfully realize reversible solid complexation of bromide components for stable non-flow zinc-bromine battery *** quaternary ammonium groups(NV^(4+)ions)can effectively capture the soluble polybromide species based on strong chemical interaction and realize reversible solid complexation confined within the porous electrodes,which transforms the conventional“liquid-liquid”conversion of soluble bromide components into“liquid-solid”model and effectively suppresses the shuttle ***,the developed non-flow zinc-bromide battery provides an outstanding voltage platform at 1.7 V with a notable specific capacity of 325 mAh g^(-1)NVBr4(1 A g^(-1)),excellent rate capability(200 mAh g^(-1)NVBr4 at 20 A g^(-1)),outstanding energy density of 469.6 Wh kg^(-1)and super-stable cycle life(20,000 cycles with 100%Coulombic efficiency),which outperforms most of reported zinc-halogen *** mechanism analysis and DFT calculations demonstrate that the chemical interaction of quaternary ammonium groups and bromide species is the main reason for suppressing the shuttle *** developed strategy can be extended to other halogen batteries to obtain stable charge storage.

关键词： Solid-liquid conversion Self-capture Non-ﬂow zinc-bromine batteries Quaternary ammonium

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Transformer-Based Fusion of RGB and Depth Images for Terrain Recognition

Transformer-Based Fusion of RGB and Depth Images for Terrain...

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International Conference on Biomimetic Intelligence and robotics & Medical robotics Forum, ICBIR+MRF 2024

作者： Chen, Hui Chen, Chunjie Wang, Zhuo Yang, Fangliang Pan, Heng Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China

This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation. © 2024 The Authors.

关键词： RGB color model

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Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-LSTM-Attention Network from Surface Electromyography

Accurate Prediction of Knee Joint Angles Using a Hybrid CNN-...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Wang, Zhuo Chen, Hui Yang, Fangliang Wang, Xiangyang Wu, Xinyu Chen, Chunjie Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665481090

In various fields such as robotics, rehabilitation, biomechanics, human-machine interfaces, and clinical research, human motion prediction has extensive applications. A hybrid networks model based on Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention) was employed in this paper to extract features from the high-dimensional space and time series of surface electromyography (sEMG) data for continuous prediction of knee angles. Five able-bodied subjects participated in the study, walking at a self-selected pace while six surface electromyography (sEMG) signals from muscles involved in knee flexion and extension, along with knee angles, were synchronously recorded. The performance was assessed using the root mean square error (RMSE), Pearson Correlation Coefficient (r), and coefficient of determination (R2) between estimated and measured knee joint angles. Compared to the CNN, LSTM, and CNN-LSTM algorithms, the adopted method estimated knee angles more accurately, achieving an average RMSE, ρ, and R2 of 3.09± 0.90,\ 0.9911 ± 0.0052, and 0.9817± 0.0108, respectively. As the advance time increased, the accuracy of knee joint angle estimation decreased, particularly when the advance time reached 100 ms, at which point the RMSE is 4.83 degress. In addition, the computing time of the prediction model was less than 1 ms, which was shorter than the advance time 100 ms it could predict, thereby facilitating real-time computation in practical environments. Compared to previous studies, the proposed method excelled at extracting features directly from raw signals, avoiding the need for handcrafted feature extraction. Consequently, the model effectively achieved continuous and accurate predictions of joint motion angles and demonstrated potential to reduce latency in the human-machine interface. © 2024 IEEE.

关键词： Clinical research

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A New Recursive Composite Adaptive Controller for Robot Manipulators

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Space(Science & technology) 2021年第1期2021卷 77-83页

作者： Jianfei Li Yaobing Wang Zhiyong Liu Xin Jing Chengwei Hu The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Intelligent Space Robotic System Technology and Applications Beijing Institute of Spacecraft System EngineeringBeijing 100094China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

In this paper,a new recursive implementation of composite adaptive control for robot manipulators is *** investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form,which,to our knowledge,is the first result on this point in the *** proposed algorithm has an amount of computation O(n),which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree *** manipulator of the Chinese Space Station is employed as a simulation example,and the results verify the effectiveness of this proposed recursive algorithm.

关键词： Robot manipulators robot

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Highly stable and wide-range flexible pressure sensors with a gradient modulus and selective foaming layer

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Chemical Engineering Journal 2025年 515卷

作者： Wang, Meng Li, Wendong Lv, Yadong Li, Guangxian Zhao, Zhijun Wang, Yaobing Zhou, Qinghua Pu, Wei Kong, Miqiu School of Aeronautics and Astronautics Key Laboratory of Advanced Spatial Mechanism and Intelligent Spacecraft Ministry of Education National Key Laboratory of Advanced Polymer Materials Sichuan University Chengdu610065 China College of Polymer Science and Engineering National Key Laboratory of Advanced Polymer Materials Sichuan University Chengdu610065 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China

The service life and stability of flexible pressure sensors vary with the interfacial toughness of these multilayered structures, which are principally fabricated by adhesive bonding, hot melting, and interconnection. However, balancing the trade-off between interface toughness and sensing performance under complex stresses remains challenging. To address this issue, a foam sandwich structure with gradient modulus is constructed by integrating a thermoplastic polyurethane (TPU)/carbon nanotube electrode with a selectively foamed TPU/ionic liquid dielectric. The synthesis is fundamentally inspired by the molecular chain entanglements and hydrogen bonds formed at the electrode/dielectric interface owing to solvent diffusion due to secondary solubilization and differential foaming temperatures. The synthesized sensing structure exhibits ultra-high toughness (3000 J/m2) and dynamic impact resistance (1 J);meanwhile, the sensor shows an ultra-wide sensing range (0.02–2000 kPa), high sensitivity (1.7 nF/kPa), and excellent service stability up to 50,000 cycles under compressive stress (100 kPa), shear stress (30 kPa), bending stress (bending radius of 1.6 mm), and complex stress of 120 kPa (compression stress of 116 kPa and shear stress of 31 kPa). Additionally, on integration into robot grippers, the sensor provides accurate feedback on multi-stage object gripping and can resist shear stress while gripping high-weight objects. This study provides a new strategy for fabricating flexible pressure sensors that can be effectively used under complex stresses. © 2025 Elsevier B.V.

关键词： Shear bands

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Manual Alignment of Dual End-Effectors for Piezo-Driven Microhand

Manual Alignment of Dual End-Effectors for Piezo-Driven Micr...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Pang, Jichao Liu, Dan Tang, Xiaoqing Chen, Yan Huang, Qiang Arai, Tatsuo Liu, Xiaoming Key Laboratory of Biomimetc Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems School of Mechatronical Engineerin Beijing Institute of Technology Beijing100081 China The University of Electro-Communications Center for Neuroscience and Biomedical Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798350372601

In recent years, micromanipulator with dual end-effectors called microhand has been widely used in industrial and biological fields. Whether the tip positions of the two end-effectors of the microhand can be accurately aligned seriously affects the precise grasping of the microhand. However, few researchers have presented practical ways for precisely aligning the tip positions of a 6-DOF microhand's two end- effectors. This paper proposes a novel, inexpensive, lightweight, robust, and easy-to-use microhand dual end-effectors clamping device that can precisely and quickly align the two end-effectors of a 6-DOF microhand. First, we describe the microhand dual end-effectors clamping device's lightweight structural design. After that, we simplify the microhand dual end-effectors clamping device model and create a mathematical model to determine the relationship between the end-effectors' tip position and the rotation angles of screw No.1, screw No.2, screw No.3 of the microhand dual end-effectors clamping device. Through experimentation, we are able to attain precise alignment of the two end-effectors tip positions of the 6-DOF microhand, so we confirm that our mathematical model is true. Finally, we use the impulse response of PZT in the microhand's lower module to confirm the stability of the microhand dual end- effectors clamping device. We believe that the proposed the microhand dual end-effectors clamping device has great potential in industrial and biological applications as a universal tool for precise alignment of the tip positions of the two end-effectors of a 6-DOF microhand. © 2024 IEEE.

关键词： Clamping devices

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EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-LSTM Hybrid Model

EMG Based Rehabilitation Gesture Recognition Using DAE-CNN-L...

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2024 International Convention on Rehabilitation Engineering and Assistive technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Cao, Wujing Guo, Xinqiang Zou, Yupeng Zhang, Shuo Luo, Mingxiang Kobsiriphat, Worawarit Wu, Xinyu Yin, Meng Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518005 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems China School of Mechanical Engineering China University of Petroleum China National Metal and Materials Technology Center Thailand

ISBN: (纸本)9798350355123

Stroke, as one of the leading causes of long-term disability globally, often results in motor impairments, particularly in the hands, significantly affecting patients' daily activities and causing profound psychological trauma. Rehabilitation gesture recognition, as one of the key means of active rehabilitation medicine, holds significant potential in stroke rehabilitation, providing real-time feedback on patient progress and enabling personalized interventions to meet individual needs. Traditional gesture recognition methods face numerous challenges when dealing with stroke patient data, such as noise, motion blur, and individual differences. To address these challenges, this paper proposes a novel approach for stroke patient gesture recognition using deep learning models. We adopt a strategy combining denoising autoencoder (DAE), convolutional neural network (CNN), and long short-term memory (LSTM) to enhance the accurate recognition of hand movements in stroke patients. Specifically, DAE is utilized for denoising EMG signals, extracting features, and reducing noise to improve the signal's noise resistance. CNN is employed for spatial feature extraction, while LSTM captures temporal dependencies in gesture sequences. By integrating these three deep learning models, our aim is to enhance the accuracy and robustness of rehabilitation gesture recognition. We validate the proposed method's effectiveness on an EMG dataset from seven subjects through experiments and compare it with traditional machine learning and individual CNN and LSTM algorithms. Experimental results demonstrate significant performance improvements of our hybrid model in rehabilitation gesture recognition tasks, indicating promising application prospects and practical significance. © 2024 IEEE.

关键词： Convolutional neural networks

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基于细胞内应变评估的卵母细胞去核方法及在机器人化克隆中的应用

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Engineering 2023年第5期24卷 73-83,I0002页

作者： Ming-Zhu Sun Yao-Wei Liu Mao-Sheng Cui Qi-Li Zhao Xiang-Fei Zhao Yi-Di Zhang Jing-Jing Huang Gui-Zhang Lu Xin Zhao Institute of Robotics and Automatic Information System Tianjin Key Laboratory of Intelligent RoboticsNankai UniversityTianjin 300071China Tianjin Animal Science and Veterinary Research Institute Tianjin 300201China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai UniversityShenzhen 518063China

自1996年第一只克隆羊诞生,克隆技术就由于其在动物育种方面的巨大潜力而得到了大量关注。体细胞核移植技术是动物克隆的核心,其操作过程非常复杂,不可避免地造成细胞内损伤。通常来说,只有不到1%的重构胚胎能够发育成克隆动物,克隆的... 详细信息

自1996年第一只克隆羊诞生,克隆技术就由于其在动物育种方面的巨大潜力而得到了大量关注。体细胞核移植技术是动物克隆的核心,其操作过程非常复杂,不可避免地造成细胞内损伤。通常来说,只有不到1%的重构胚胎能够发育成克隆动物,克隆的低成功率是制约其广泛应用的主要原因之一。为此,本文提出了一种基于细胞内应变评估的卵母细胞去核方法,来减少体细胞核移植中的潜在细胞内损伤,从而提高克隆成功率。首先,论文根据细胞内速度场计算细胞内应变,进而将内应变作为评价标准来优化去核操作;在此基础上,开发了一套机器人批量体细胞核移植系统,用于实现机器人化克隆。实验结果表明,本文方法将体细胞核移植后的胚胎囊胚率从10.0%提高到20.8%,并成功获得17头克隆小猪,这是世界首批由机器人完成克隆操作获得的克隆动物。对比手动核移植操作,本文将克隆成功率从平均0.73%提升到了2.50%,大幅提升了克隆效率。与此同时,基于细胞内应变的去核方法有望应用于其他生物操作,并建立一种通用的减少细胞内损伤的细胞操作规范。

关键词：动物育种去核方法体细胞核移植克隆动物卵母细胞机器人化动物克隆克隆技术

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