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3rd International Conference on Automation, robotics and Computer Engineering, ICARCE 2024

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

ISBN: (纸本)9798331529505

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. © 2024 IEEE.

关键词： Industrial robots

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Using Hip Assisted Running Exoskeleton with Impact Isolation Mechanism to Improve Energy Efficiency

Using Hip Assisted Running Exoskeleton with Impact Isolation...

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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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Human-Exoskeleton Locomotion Interaction Experience Transfer: Speeding up and Improving the Performance of Preference-based Optimizations of Exoskeleton Assistance During Walking

Human-Exoskeleton Locomotion Interaction Experience Transfer...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hongwu Li Junchen Liu Ziqi Wang Haotian Ju Tianjiao Zheng Yongsheng Gao Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we propose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET framework could speed up the exploration process and acquire more satisfying optimized results for naive wearers.

关键词： Legged locomotion Exoskeletons Process control Optimization methods robotics and automation

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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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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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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

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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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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Driving deep-learning-based metasurface design with Kramers-Kronig relations

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Physical Review Applied 2024年第4期22卷 L041002页

作者： Guangfeng You Chao Qian Shurun Tan Erping Li Hongsheng Chen ZJU-UIUC Institute Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University

Two-decade strides in metasurfaces have enabled a multitude of theoretical breakthroughs and experimental discoveries that outride established comprehension. Deep learning has recently found favor for expediting metasurface design and unearthing complex light-matter interactions, in contrast to resource-intensive numerical simulations. However, most “black box” algorithms lack enough discernment on parsing internal physical connections. Here, we propose a physical adversary channel to be complementary to gradient descent channel by embedding the Kramers-Kronig (KK) relations into a neural network, quantifying the inherent spectral contradictions at the output side. We evaluate the superiority of the KK-driven neural network in forward prediction and inverse metasurface design by modifying loss function and shaping probability distribution in latent space, respectively. The exceptional outcome suggests that the similarity between output and given spectra reaches up to 99.8% and maintains an extremely high fidelity even in a mutant band. Our work provides a physically explicable perspective to explain “black box” models, possibly reviving intelligent metasurface applications.

关键词： Inverse problems Artificial neural networks Metamaterials Metasurfaces Deep learning Electromagnetic wave theory

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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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