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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Ziqi Wang Junchen Liu Hongwu Li Qinghua Zhang Xianglong Li Yi Huang Haotian Ju Tianjiao Zheng Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Research has indicated that exoskeletons can assist human movement, but due to the influence of additional weight and challenges in control strategy design, only a few exoskeletons effectively reduce the wearers’ metabolic costs during running. This paper proposes an innovative and efficient hip-assisted running exoskeleton (HARE) designed to facilitate the flexion and extension movements of the joint along the sagittal plane. In the field of structural engineering, we propose implementing an active-passive combination constant force suspension system, hereinafter referred to as CFS, to effectively mitigate the impact of inertial forces during running. The decoupled transmission mechanism allows the CFS and assist mechanisms to operate independently, ensuring the tension of the cables. The flexible structural design can reduce the locomotion limitation on human bodies and reduce the additional energy burden on the body. In control strategy designing, the joint torque-generating strategy provides personalized assistance strategies for wearers to actively optimize the control parameters. Meanwhile, the safety control strategy based on abnormal gait recognition can ensure human safety. Experiments have shown that compared to not wearing exoskeletons, this device can reduce the energy consumption of the human body by 5.33 % at a speed of 9 km/h. This demonstrates its potential in human motion assistance processes.

关键词： Energy consumption Costs Exoskeletons Force Energy efficiency Safety Structural engineering Intelligent robots Hip Gait recognition

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Direct Electrical Heating and Multi-Cycle Stretching Method for Micro Wire Straightening

Direct Electrical Heating and Multi-Cycle Stretching Method ...

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IEEE International Conference on Nano/Micro Engineered and Molecular systems (NEMS)

作者： Yan Xu Xianghe Meng Xingjian Shen Xiaomo Wu Hui Xie The State key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350359831

ISBN: (纸本)9798350359848

Wire straightening is essential to produce straight wires from a coil for micro-needle-assisted implantation of flexible neural electrodes in brain-computer interfaces. This article introduces a novel direct electrical heating and multi-cycle stretching (DEHMS) method, offering advancements over the current state of the art. For the first time, it has been possible to straighten tungsten wires with diameters smaller than 40 μm effectively, significantly minimizing deflection through multi-cycle stretching and direct current heating. The straightening device was housed in an inert gas chamber to prevent surface oxidation. A force sensor monitored the tension in real-time during the process, providing feedback to the motion module to accurately adjust the wire's stretching and releasing. Our investigation into tension variation, stretch-release cycles, and applied voltage showed that achieving optimal straightness (≈1 μm/1000 μm) minimizes positioning errors caused by the non-straightness of tungsten wires for cortical electrode implantation to below a neuronal cell's length. Optimal conditions were found to be 1.6 N tension, 8 V, and 14 stretch-release cycles in an argon atmosphere.

关键词： Electrodes Tungsten Voltage Brain-computer interfaces Real-time systems Wire Argon

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An Iterative Variable Step Error Compensation Approach for Enhancing Robot Trajectory Accuracy

An Iterative Variable Step Error Compensation Approach for E...

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Automation, robotics and Computer Engineering (ICARCE), International Conference on

作者： Chengzhi Wang Sikai Zhao Tianjiao Zheng Hegao Cai Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

In this paper, an iterative error compensation approach is analyzed and designed to improve trajectory accuracy of industrial robots. Advanced offline programming and robotic machining tasks require high trajectory accuracy of industrial robots, which is significantly affected by parameter inaccuracy, stiffness deformation, backlash, and so on. Such complicated coupling makes it impossible to model each factor separately, thus data-driven direct error compensation is the major solution to improve trajectory accuracy. Previous methods utilize external measurement equipment to obtain tracking error and rely on the error consistency assumption to compensate for the error by modifying the trajectory commands directly. However, such consistency assumption does not hold. Even a minor incremental modification of the initial commands can lead to unexpected influence on the robot's tracking performance, owning to unaccounted factors such as the robot's dynamic stiffness and natural frequency. This paper decomposes trajectory tracking errors based on servo encoder feedback and external laser tracker, and proposes a variable step error compensation scheme to improve tracking performance. Physical experiments are conducted on a standard 6 degree-of-freedom industrial robot to validate the effectiveness of the proposed method. The results of various error compensation approaches are analyzed to draw a possible error correlation model, in order to account for the error inconsistency in the compensation process.

关键词： Accuracy Service robots Trajectory tracking Measurement uncertainty Error compensation Industrial robots Laser modes Trajectory Iterative methods Load modeling

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A Diffusion Model for Traffic Data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： Data imputation diffusion model implicit feature time series traffic data

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Smoke Attention Based Laparoscopic Image Desmoking Network with Hybrid Guided Embedding

Smoke Attention Based Laparoscopic Image Desmoking Network w...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Liu, Ziteng Gao, Wenpeng Zhu, Jiahua Liu, Bainan Fu, Yili School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin150080 China

ISBN: (纸本)9781665481090

Surgical smoke can obscure the surgeon's line of sight during the laparoscopic procedure, potentially posing risk to the patients. Robot assisted-minimally invasive surgery (MIS) also faces such a challenge, which also hinders its autonomous operation. This paper proposes a desmoking method for laparoscopic surgery smoke removal. As the surgical smoke is hetergeneous, a two-stage network is developed to estimate the smoke distribution and reconstruct the corresponding smoke-free scene with the hybrid embedding guidance, including the estimated smoke mask and the initial image. The network is designed following the concept of the atmospheric scattering model, which makes the network more explainable. To qualitatively and quantitatively evaluate the proposed method, a synthetic laparoscopic dataset is generated and a real laparoscopic dataset is obtained. Experimental results show that the Peak Signal to Noise Ratio (PSNR) of the proposed method is 1.9% higher than that of the DehazeFormer, while the run-time is shorter more than 40.9%. The proposed method is comparable to or even outperforms the state-of-the-arts in desmoking quality and computation cost. © 2024 IEEE.

关键词： Laparoscopy

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Microstructure recognition of steels by machine learning based on visual attention mechanism

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Journal of Iron and Steel Research International 2024年第4期31卷 909-923页

作者： Xing-yu Chen Lin Cheng Cheng-yang Hu Yu-peng Zhang Kai-ming Wu The State Key Laboratory of Refractories and Metallurgy Hubei Province Key Laboratory of Systems Science on Metallurgical ProcessingInternational Research Institute for Steel TechnologyCollaborative Center on Advanced SteelsWuhan University of Science and TechnologyWuhan 430081HubeiChina Metals Valley and Band(Foshan)Metallic Composite Materials Co. Ltd.Foshan 528000GuangdongChina

U-Net has achieved good performance with the small-scale datasets through skip connections to merge the features of the low-level layers and high-level layers and has been widely utilized in biomedical image segmentation as well as recent microstructure image segregation of the *** representative visual attention mechanism modules,named as squeeze-and-excitation networks,convolutional block attention module,and extended calibration algorithm,were intro-duced into the traditional U-Net architecture to further improve the prediction *** is found that compared with the original U-Net architecture,the evaluation index of the improved U-Net architecture has been significantly improved for the microstructure segmentation of the steels with the ferrite/martensite composite microstructure and pearlite/ferrite composite microstructure and the complex martensite/austenite island/bainite microstructure,which demonstrates the advantages of the utilization of the visual attention mechanism in the microstructure *** reasons for the accuracy improvement were discussed based on the feature maps analysis.

关键词： Microstructure recognition-Steel Machine learning Visual attention mechanism Visualization

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Optimal Dynamical Gauge in the Quantum Rabi Model

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Chinese Physics Letters 2025年第2期42卷 13-25页

作者： Yuqi Qing Liangsheng Li Wen-Long You Yueheng Lan Maoxin Liu State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijing 100876China Science and Technology on Electromagnetic Scattering Laboratory Beijing 100854China College of Science Nanjing University of Aeronautics and AstronauticsNanjing211106China School of Systems Science/Institute of Nonequilibrium Systems Beijing Normal UniversityBeijing 100875China

We have investigated the gauge dependence of physical observables in a quantum Rabi model under different potential fields arising from the Hilbert space truncation of the atomic degrees of *** both the square-well and harmonic potentials,the optimal gauge for the ground-state energy depends on the cavity frequency,which is optimal in the low-frequency limit,whereas the Coulomb gauge is optimal in the high-frequency *** a dynamic quantity,such as an out-of-time-order correlator,we have demonstrated the necessity of introducing an optimal dynamic *** study provides deeper insight into the intricate relationship between gauge choice and the dynamics of quantum electrodynamics systems,resulting in more accurate theoretical frameworks.

关键词： gauge optimal quantum

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On the Design of NOMA-Based Integrated Sensing and Communications (ISAC) in Near-Field Extremely Large-Scale MIMO systems

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IEEE Internet of Things Journal 2025年第12期12卷 18554-18569页

作者： Sun, Like Zhao, Zhongyuan Jia, Chao Quek, Tony Q. S. Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Information and Communication Engineering Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines 487372 Singapore

Integrated sensing and communications (ISAC) have been widely applied in Internet of Things (IoT) networks for their capability to simultaneously support high-performance communication and sensing, and nonorthogonal multiple access (NOMA) is introduced to further improve spectral efficiency and connection density. However, with the deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) and high-frequency (HF) technologies, the near-field (NF) paradigm replaces the conventional far-field (FF) paradigm and becomes dominant, necessitating a reassessment of NOMA-based ISAC system performance in the NF region. To address this, a novel NOMA-based ISAC scheme in NF XL-MIMO systems is proposed in this article, wherein a multibeam design based on subarray partitioning is employed to realize a communication-and-sensing coexistence ISAC system, while the additional distance-based Degree of Freedom (DoF) provided by the unique NF beamfocusing is utilized to improve NOMA performance gains. To balance the optimal performance tradeoff between communication and sensing, an optimization problem for joint device scheduling, subarray partitioning, and power allocation is formulated to maximize the ISAC joint rate under various constraints. Based on alternating optimization (AO) and fractional programming (FP) techniques, an efficient joint optimization algorithm is developed to solve the complex nonconvex problem with coupled variables. In particular, the original problem is decoupled into three subproblems and solved by applying the linearization of 0-1 polynomial programming, Lagrangian dual reformulation, and quadratic transform techniques. Numerical results validate that our proposed NOMA-based ISAC scheme and joint optimization algorithm significantly enhance the ISAC joint rate performance in NF XL-MIMO systems for IoT networks. © 2014 IEEE.

关键词： Quadratic programming

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MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Ma, Yongxin Xu, Jie Yuan, Shenghai Zhi, Tian Yu, Wenlu Zhou, Jun Xie, Lihua school of Mechanical Engineering Shandong University Jinan China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China

SLAM plays a crucial role in automation tasks, such as warehouse logistics, healthcare robotics, and restaurant delivery. These scenes come with various challenges, including navigating around crowds of people, dealing with flying plastic bags that can temporarily blind sensors, and addressing reduced LiDAR density caused by cooking smoke. Such scenarios can result in over-degeneracy, causing the map to drift. To address this issue, this paper presents a multi-map LiDAR-inertial system (MM-LINS) for the first time. The front-end employs an iterated error state Kalman filter for state estimation and introduces a reliable evaluation strategy for degeneracy detection. If over-degeneracy is detected, the active map will be stored into sleeping maps. Subsequently, the system continuously attempts to construct new maps using a dynamic initialization method to ensure successful initialization upon leaving the over-degeneracy. Regarding the back-end, the Scan Context descriptor is utilized to detect inter-map similarity. Upon successful recognition of a sleeping map that shares a common region with the active map, the overlapping trajectory region is utilized to constrain the positional transformation near the edge of the prior map. In response to this, a constraint-enhanced map fusion strategy is proposed to achieve high-precision positional and mapping results. Experiments have been conducted separately on both public datasets that exhibited over-degenerate conditions and in real-world environments. These tests demonstrated the effectiveness of MM-LINS in over-degeneracy environment. Our codes are open-sourced on Github. IEEE

关键词： Optical radar

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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