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A flat-foldable equiangular spiral folding pattern inspired by sunflowers for deployable structures

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Chinese Journal of Aeronautics 2024年第6期37卷 425-438页

作者： Shuangqing YU Jinguo LIU Pengyuan ZHAO Yingying TANG State Key Laboratory of robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China institutes for robotics and intelligent manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China

Flasher origami pattern has been widely utilized to improve the stowage efficiency of deployable ***,flasher origami cannot be folded fully flat,and they still have great potential for optimization in terms of storage volume and folding *** this paper,a flat foldable equiangular spiral folding pattern inspired by the sunflower disk is ***,a parametric design method for this equiangular spiral crease diagram is introduced in ***,a kinematic model of the equiangular spiral folding pattern is established based on the kinematic equivalence between rigid origami and spherical linkages.A simulation of the developed model demonstrates that the equiangular spiral folding pattern can be folded *** the folded ratio as an evaluation index,the calculated results and experiments show that the equiangular spiral crease pattern can yield fewer creases and improve stowage efficiency in comparison to flasher origami *** spiral folding pattern can save a considerable amount of space and provide a new approach to spatially deployable structures.

关键词： Flasher origami Sunflower-inspired Deployable structures Flat-foldability Equiangular spiral folding pattern

Vision-based docking system for an aromatic-hydrocarbon-inspired reconfigurable robot

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Science China(Technological Sciences) 2024年第6期67卷 1798-1816页

作者： XING FangYi XU Cheng LIU JinGuo XUE ZhiHui State Key Laboratory of robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang110158China institutes for robotics and intelligent manufacturing Chinese Academy of SciencesShenyang110169China University of Chinese Academy of Sciences Beijing100049China

Aromatic hydrocarbons generally refer to compounds containing benzene *** types of isomers can be formed by replacing hydrogen atoms on the benzene *** this paper,an aromatic-hydrocarbon-inspired modular robot(AHIMR)is *** robot can be reassembled into different configurations suitable for various task requirements.A vision-based docking system is designed for the *** system primarily consists of two stages:a remote guidance stage and a precise docking *** the remote guidance stage,an object module is identified using an illumination adaptive target recognition algorithm,and then the active module moves to the docking area through communication with *** the precise docking stage,the active module calculates the relative pose with the object module using a perspective-n-point method and dynamically adjusts its posture to *** this process,a Kalman filter is used to reduce target occlusion and jitter *** addition,the docking system feasibility is verified via several simulation *** module docking accuracy is controlled within 0.01 m,which meets the reconfiguration task requirements of the *** the AHIMR submodule docking experiment,the active module accurately moves to the expected position with a docking success rate of 95%.

关键词： modular robot reconfiguration aromatic hydrocarbon autonomous docking visual identification

Efficient nonnegative tensor decomposition using alternating direction proximal method of multipliers

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Chinese Journal of Electronics 2024年

作者： Deqing WANG Guoqiang HU State Key Laboratory of robotics Shenyang Institute of Automation Chinese Academy of Sciences institutes for robotics and intelligent manufacturing Chinese Academy of Sciences Key Laboratory of Marine robotics College of Artificial Intelligence Dalian Maritime University

Nonnegative CANDECOMP/PARAFAC（NCP） tensor decomposition is a powerful tool for multiway signal processing. The optimization algorithm alternating direction method of multipliers（ADMM） has become increasingly popular for solving tensor decomposition problems in the block coordinate descent framework. However,the ADMM-based NCP suffers from rank deficiency and slow convergence for some large-scale and highly sparse tensor data. The proximal algorithm is preferred to enhance optimization algorithms and improve convergence properties. In this study, we propose a novel NCP algorithm using the alternating direction proximal method of multipliers（ADPMM） that consists of the proximal algorithm. The proposed NCP algorithm can guarantee convergence and overcome the rank deficiency. Moreover, we implement the proposed NCP using an inexact scheme that alternatively optimizes the subproblems. Each subproblem is optimized by a finite number of inner iterations yielding fast computation speed. Our NCP algorithm is a hybrid of alternating optimization and ADPMM and is named *** experimental results on synthetic and real-world tensors demonstrate the effectiveness and efficiency of our proposed algorithm.

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DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

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Computers, Materials & Continua 2024年第2期78卷 1755-1771页

作者： Pengchao Li Fang Xu Jintao Wang Haibing Guo Mingmin Liu Zhenjun Du State Key Laboratory of robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China institutes for robotics and intelligent manufacturing Chinese Academy of SciencesShenyang110169China University of Chinese Academy of Sciences Beijing100049China Shenyang SIASUN Robot&Automation Co. Ltd.Shenyang110168China

We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent ***,to enhance the capability of deep neural networks in extracting geometric attributes from depth images,we developed a novel deep geometric convolution operator(DGConv).DGConv is utilized to construct a deep local geometric feature extraction module,facilitating a more comprehensive exploration of the intrinsic geometric information within depth ***,we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network(FCN8)to establish a high-performance deep neural network algorithm tailored for depth image ***,we enhance the FCN8 detection head by separating the segmentation and classification *** enhancement significantly boosts the network’s overall detection ***,for a comprehensive assessment of our proposed algorithm and its applicability in real-world industrial settings,we curated a line-scan image dataset featuring weld *** dataset,named the Standardized Linear Depth Profile(SLDP)dataset,was collected from actual industrial sites where autonomous robots are in ***,we conducted experiments utilizing the SLDP dataset,achieving an average accuracy of 92.7%.Our proposed approach exhibited a remarkable performance improvement over the prior method on the identical ***,we have successfully deployed the proposed algorithm in genuine industrial environments,fulfilling the prerequisites of unmanned robot operations.

关键词： Weld image detection deep learning semantic segmentation depth map geometric feature extraction

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

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Computers, Materials & Continua 2024年第6期79卷 4471-4489页

作者： Suyi Liu Jianning Chi Chengdong Wu Fang Xu Xiaosheng Yu Faculty of Robot Science and Engineering Northeastern UniversityShenyang110167China State Key Laboratory of robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China institutes for robotics and intelligent manufacturing Chinese Academy of SciencesShenyang110169China SIASUN Robot&Automation Co. Ltd.Shenyang110169China

In recent years,semantic segmentation on 3D point cloud data has attracted much *** 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently ***,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud *** current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation *** this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range ***,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive *** addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is *** demonstrate the validity and superiority of our method on the S3DIS and ShapeNet *** ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

关键词： 3D point cloud semantic segmentation long-range contexts global-local feature graph convolutional network dense-sparse sampling strategy

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina institutes for robotics and intelligent manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

Strain sensor on a chip for quantifying the magnitudes of tensile stress on cells

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Microsystems & Nanoengineering 2024年第3期10卷 305-315页

作者： Yuyin Zhang Yue Wang Hongze Yin Jiahao Wang Na Liu Songyi Zhong Long Li Quan Zhang Tao Yue School of Mechatronics Engineering and Automation Shanghai UniversityShanghaiChina School of Future Technology Shanghai UniversityShanghaiChina Key Laboratory of Advanced manufacturing Technology of Zhejiang Province School of Mechanical EngineeringZhejiang UniversityHangzhouChina Shanghai Key Laboratory of intelligent manufacturing and robotics Shanghai UniversityShanghaiChina Shanghai Institute of intelligent Science and Technology Tongji UniversityShanghaiChina

During cardiac development,mechanotransduction from the in vivo microenvironment modulates cardiomyocyte growth in terms of the number,area,and arrangement ***,the response of cells to different degrees of mechanical stimuli is ***-on-a-chip,as a platform for investigating mechanical stress stimuli in cellular mimicry of the in vivo microenvironment,is limited by the lack of ability to accurately quantify externally induced ***,previous technology lacks the integration of external stimuli and feedback sensors in microfluidic platforms to obtain and apply precise amounts of external ***,we designed a cell stretching platform with an in-situ *** in-situ liquid metal sensors can accurately measure the mechanical stimulation caused by the deformation of the vacuum cavity exerted on *** platform was applied to human cardiomyocytes(AC16)under cyclic strain(5%,10%,15%,20 and 25%),and we found that cyclic strain promoted cell growth induced the arrangement of cells on the membrane to gradually unify,and stabilized the cells at 15%amplitude,which was even more effective after 3 days of *** platform’s precise control and measurement of mechanical forces can be used to establish more accurate in vitro microenvironmental models for disease modeling and therapeutic research.

关键词： stress deformation arrangement

Assistive robots for Beijing Winter Paralympic torch relay: Accessible technologies to restore human functionality

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The Innovation 2024年第1期5卷 19-20页

作者： Bi Zhang Mei Shuai Bicheng Han Shuo Li Lianqing Liu Xingang Zhao State Key Laboratory of robotics Shenyang Institute of AutomationCASShenyang 110016China institutes for robotics and intelligent manufacturing CASShenyang 110169China Department of Biomedical Engineering Beihang UniversityBeijing 100191China Beijing AI-robotics Technology Co. Ltd.Beijing 100176China Zhejiang BrainCo Technology Co. Ltd.Hangzhou 311121China

The success of the Beijing Winter Olympics and Paralympics epitomized the vision of a community with a shared future for *** robots and artificial intelligence technologies were used in various games and events,such as assisting in parades,logistics,and security *** integration of unmanned robotic systems into torch relays was one of the highlights of the Winter Olympics.

关键词： Winter Access artificial

Stable watt-level mode-locked noise-like pulse from an all-PM fiber oscillator at 2 μm

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Chinese Optics Letters 2024年第6期 159-162页

作者：王蒙丁俊嘉欧阳德钦陈业旺赵俊清刘星阮双琛 Key Laboratory of Advanced Optical Precision manufacturing Technology of Guangdong Higher Education institutes Sino-German College of Intelligent ManufacturingShenzhen Technology University

We have experimentally presented a watt-level noise-like (NL) pulse mode-locked all-polarization-maintaining(PM) fiber laser centered at ～1995 nm, which can directly generate stable NL pulses with a maximum output power of ～1.017 W and pulse energy of ～0.61 μJ, representing the highest output power of mode-locked NL pulse at 2 μm from any fiber oscillators,to the best of our knowledge. The mode-locked NL pulse laser exhibits an excellent stability with a power fluctuation of～0.1% in 8 h of monitoring, and a signal-to-noise ratio of ～83 dB at a fundamental frequency of ～1.662 MHz. Moreover, the pulse envelope and coherence spike width of the NL pulse can be widely tuned from ～4.5 ns to ～16 ns, and ～364 fs to～323 fs, respectively, with the enhancement of the pump power. Such an all-PM fiber oscillator is the ideal seed source for the implementation of a high-power NL pulse laser and has potential valuable applications in mid-infrared spectroscopy and industrial processing.

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