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Iterative assist-as-needed control with interaction factor for rehabilitation robots

Science China(Technological Sciences) 2021年第4期64卷 836-846页

作者： CAO Ran CHENG Long YANG ChenGuang DONG Zhe School of Artificial Intelligence University'of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Institute of Automation.Chinese Academy of SciencesBeijing 100190China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China School of Electrical and Control Engineering North China Universityof TechnologyBeijing 100144China

Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system’s stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient’s motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

关键词： human-robot interaction impedance stiffness control rehabilitation

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A Multi-view Non-negative Matrix Factorization Single-cell Clustering Algorithm for Missing Data

A Multi-view Non-negative Matrix Factorization Single-cell C...

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2022 International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2022

作者： Yu, Ailian Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781665454728

ISBN: (纸本)9781665454728

A significant study focus on the field of bioinformatics has been on how to quickly and accurately accomplish the cell clustering task using computational approaches for single-cell RNA sequencing (scRNA-seq) data. Technical issues with scRNA-seq data include missing values, high noise levels, and a large batch impact. While the single-cell data recovery method based on data filling can enhance data quality, it will subjectively alter the distribution of data. To address the above-mentioned issues, we propose a non-negative matrix factorization clustering algorithm (scMVNMF) based on multi-view data of scRNA-seq. Under the assumption of essentially unchanging original data distribution, it can not only identify the typical low-dimensional representation patterns of multi-view data but also produce final explicable clustering findings. The results of the experiments demonstrate that the suggested algorithm has some advantages over the filling approach in terms of clustering tasks and can maintain a certain level of stability when the quality of single-cell data varies. © 2022 IEEE.

关键词： Non-negative matrix factorization

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Point Density-aware Channel-wise Transformer for LiDAR 3D Object Detection 20

Point Density-aware Channel-wise Transformer for LiDAR 3D Ob...

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20th IEEE International Conference on Mechatronics and automation, ICMA 2023

作者： Yang, Fanhang Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Xiqing District Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

We present a two-stage 3D object detection framework from point clouds, named Point Density-aware Channel-wise Transformer (PD-CT3D), which investigate the property of point density. This architecture uses 3D sparse CNN to effectively generate high-quality proposals at the first stage, then integrates the inherent property of point density and 3D CNN-based voxel features by density-aware proposal grid pooling. Specifically, each generated proposal from the first stage is divided into grids to aggregate corresponding voxel-wise features and raw point-based features for encoding the representative features from the whole scene. Subsequently, a channel-wise encoder-decoder transformer is adopted to extract the encoded density-aware features and decode them into a global representation for final refinement. Experiments on the widely used KITTI dataset show that the PD-CT3D achieves competitive performance among state-of-the-art methods. © 2023 IEEE.

关键词： Object detection

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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Deformation Pattern Shape Sensing for Visuotactile Endoscope

Deformation Pattern Shape Sensing for Visuotactile Endoscope

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

作者： Yu, Yanzhe Tian, Yuan Cui, Shaowei Wang, Yupeng Wang, Rui Wang, Shuo Beijing University of Science and Technology Beijing100083 China The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9798350372601

The integration of visuotactile sensor technology with endoscopy enables the provision of tactile information for minimally invasive surgeries and other operations. This paper proposes a real-time sensing framework for contact deformation pattern shape, providing a foundation for precise perception in miniaturized visuotactile sensors. Specifically, we first construct a Deformation Pattern shape Descriptor (DPSD) and then track the key points in the descriptor using optical flow to obtain contact deformation shape information. Through extensive qualitative and quantitative experiments, our proposed algorithm achieves an average perception accuracy of around 83.81% for contact pattern shapes. It demonstrates consistent perception accuracy across various contact force magnitudes, directions, and scenarios, highlighting its robustness. Additionally, compared to traditional marker-level methods, it achieves a notable 46.5% improvement in displacement field perception accuracy. This research elevates tactile perception to the dimension of contact pattern shape sensing and has the potential for application in miniaturized visual tactile sensors. © 2024 IEEE.

关键词： Endoscopy

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EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry

arXiv

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arXiv 2024年

作者： Wu, Xiaoyi Chen, Yushuai Li, Zhan Hong, Ziyang Hu, Liang The Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the latest generation of FWCW radars, the 4D mmWave radar provides point cloud with range, azimuth, elevation, and Doppler velocity information, despite inherent sparsity and noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate, highly efficient, and learning-free method for large-scale 4D radar odometry estimation. EFEAR-4D exploits Doppler velocity information delicately for robust ego-velocity estimation, resulting in a highly accurate prior guess. EFEAR-4D maintains robustness against point-cloud sparsity and noises across diverse environments through dynamic object removal and effective region-wise feature extraction. Extensive experiments on two publicly available 4D radar datasets demonstrate state-of-the-art reliability and localization accuracy of EFEAR-4D under various conditions. Furthermore, we have collected a dataset following the same route but varying installation heights of the 4D radar, emphasizing the significant impact of radar height on point cloud quality - a crucial consideration for real-world deployments. Our algorithm and dataset will be available soon at https://***/CLASS-Lab/EFEAR-4D. © 2024, CC BY-NC-SA.

关键词： Millimeter waves

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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Optimization of aero-engine pipeline for avoiding vibration based on length adjustment of straight-line segment

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Frontiers of Mechanical engineering 2022年第1期17卷 100-116页

作者： Wenhao JI Wei SUN Donghai WANG Zhonghua LIU School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Department of Education of China Northeastern UniversityShenyang 110819China Shenyang Area 2nd Military Representative Room of Air Force Equipment Department Shenyang 110043China

In the design and troubleshooting of aero-engine pipeline,the vibration reduction of the pipeline system is often achieved by adjusting the hoop layout,provided that the shape of pipeline remains ***,in reality,the pipeline system with the best antivibration performance may be obtained only by adjusting the pipeline *** this paper,a typical spatial pipeline is taken as the research object,the length of straight-line segment is taken as the design variable,and an innovative optimization method of avoiding vibration of aero-engine pipeline is *** relationship between straight-line segment length and parameters that determine the geometric characteristics of the pipeline,such as the position of key reference points,bending angle,and hoop position,are derived in *** on this,the parametric finite element model of the pipeline system is *** the maximum first-order natural frequency of pipeline as the optimization objective and introducing process constraints and vibration avoidance constraints,the optimization model of the pipeline system is *** genetic algorithm and the golden section algorithm are selected to solve the optimization model,and the relevant solution procedure is described in ***,two kinds of pipelines with different total lengths are selected to carry out a case *** on the analysis of the influence of straight-line segment length on the vibration characteristics of the pipeline system,the optimization methods developed in this paper are *** show that the developed optimization method can obtain the optimal single value or interval of the straight-line segment length while avoiding the excitation *** addition,the optimization efficiency of the golden section algorithm is remarkably higher than that of the genetic algorithm for length optimization of a single straight-line segment.

关键词： length adjustment spatial pipeline aero-engine vibration avoidance optimization genetic algorithm golden section algorithm

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Stability and Bifurcation control of Fractional-Order Coupled Hindmarsh-Rose Neural Networks 42

Stability and Bifurcation Control of Fractional-Order Couple...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Feng Zhou, Feifan Wang, Wei Cai, Gang Wang, Hua O Yang, Xiuqin School of Automation China University of Geosciences Wuhan430074 China Engineering Research Center of Ministry of Education for Intelligent Technology of Earth Exploration China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Boston University Department of Mechanical Engineering BostonMA02215 United States School of Education Minzu University of China Beijing100081 China

ISBN: (纸本)9789887581543

This article investigates a fractional-order coupled Hindmarsh-Rose neural networks model. Firstly, the existence and stability of an equilibrium point in the system are verified. Then, the periodic bifurcation behavior of the system on a two-parameter plane is studied, and numerical simulations show the existence of both non -chaotic and chaotic plus periodic bifurcation behavior on the two-parameter plane. Finally, a feedback controller was designed to stabilize the bifurcation point of the delayed system and increase the stable range of the system. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Neural network models

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