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Neural Dynamic Fault-Tolerant Scheme for Collaborative Motion Planning of Dual-Redundant Robot Manipulators

IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 11189-11201页

作者： Zhijun Zhang Zhongwen Cao Xingru Li School of Automation Science and Engineering South China University of Technology Guangzhou China Key Library of Autonomous Systems and Network Control Ministry of Education Guangzhou China Institute for Super Robotics (Huangpu) Guangzhou China Jiangxi Thousand Talents Plan Nanchang University Nanchang China College of Computer Science and Engineering Jishou University Jishou China Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Laboratory) Guangzhou China Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong China School of Information Science and Engineering Changsha Normal University Changsha China Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming China

To avoid the task failure caused by joint breakdown during the collaborative motion planning of dual-redundant robot manipulators, a neural dynamic fault-tolerant (NDFT) scheme is proposed and applied. To do so, a joint fault-tolerant strategy is first designed, and it is formulated as a time-varying equality constraint. Second, combining the robot position and orientation control, joint limit constraint, joint fault-tolerant equality constraint, and considering the repetitive motion optimization criterion, a fault-tolerant framework for the dual-redundant robot manipulators based on quadratic programming (QP) is constructed. Then, a varying-parameter recurrent neural network (VP-RNN) is designed to solve the QP issue. The fault-tolerant framework and the VP-RNN constitute NDFT scheme. With the NDFT scheme, the impact of faulty joints on the whole system can be remedied by healthy joints, thereby the end-effectors of the robot can complete the given end-effector task. Finally, computer simulations and physical experiments are implemented to verify the availability, physical realizability, and accuracy of the proposed NDFT scheme in the collaborative execution of end-effector tasks. Comparative experimental results with conventional repetitive motion planning schemes based on neural networks show higher accuracy and smaller joint angle drift.

关键词： Manipulators Robots Fault tolerant systems Fault tolerance End effectors Planning Kinematics Manipulator dynamics Jacobian matrices Collaboration

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Semi-Markov Telecommunication Networks Model

Semi-Markov Telecommunication Networks Model

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2024 International Conference on Actual Problems of Electron Devices engineering, APEDE 2024

作者： Svetlov, Mikhail S. L’vov, Alexey A. Martirosyan, Arman M. Martirosyan, David M. Bagaev, Igor S. Khasanov, Semen T. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Institute of Applied Information Technologies and Communications Yuri Gagarin State Technical University of Saratov Saratov Russia Phystech-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9798350393316

This paper proposes a semi-Markov model of telecommunication network (TCN). The variant of dynamic traffic adaptive control of queuing system as a special case of TCN is considered. The main purpose of control is to minimize the average cost per unit of time to service the incoming flow of information (packets). This takes into account the different bandwidth of the channels, the processing speed of information in the channel and the information capacity of the buffers. The approach to the organization of dynamic control taking into account noise immunity (information reliability) and information security is discussed. © 2024 IEEE.

关键词： Bandwidth

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GSLB: The Graph Structure Learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Survey on depth and RGB image-based 3D hand shape and pose estimation

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Virtual Reality & Intelligent Hardware 2021年第3期3卷 207-234页

作者： Lin HUANG Boshen ZHANG Zhilin GUO Yang XIAO Zhiguo CAO Junsong YUAN Department of Computer Science and Engineering State University of New York at BuffaloNY 14260USA YouTu Lab TencentShanghai 201101China Department of Computer Science Fu Foundation School of Engineering and Applied ScienceColumbia UniversityNY 10027USA National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

The field of vision-based human hand three-dimensional(3D)shape and pose estimation has attracted significant attention recently owing to its key role in various applications,such as natural human computer *** the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks(DNNs),numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose ***,the existence of complicated hand articulation,depth and scale ambiguities,occlusions,and finger similarity remain *** this study,we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D *** RGB-D cameras,hand datasets,and a performance analysis are also discussed to provide a holistic view of recent *** also discuss the research potential of this rapidly growing field.

关键词： Hand survey 3D hand pose estimation Hand shape reconstruction Hand-object interactions RGB-D cameras

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FSC-loss: A Frequency-domain Structure Consistency Learning Approach for Signal Data Recovery and Reconstruction

arXiv

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arXiv 2025年

作者： Zhang, Liwen Miao, Zhaoji Yang, Fan Shi, Gen He, Jie An, Yu Hui, Hui Tian, Jie CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Computer Science and Engineering Southeast University Nanjing211189 China School of Engineering Medicine School of Biological Science and Medical Engineering Beihang University Beijing100191 China

A core challenge for signal data recovery is to model the distribution of signal matrix (SM) data based on measured low-quality data in biomedical engineering of magnetic particle imaging (MPI). For acquiring the high-resolution (high-quality) SM, the number of meticulous measurements at numerous positions in the field-of-view proves timeconsuming (measurement of a 37×37×37 SM takes about 32 hours). To improve reconstructed signal quality and shorten SM measurement time, existing methods explore to generating high-resolution SM based on time-saving measured low-resolution SM (a 9 × 9 × 9 SM just takes about 0.5 hours). However, previous methods show poor performance for high-frequency signal recovery in SM. To achieve a high-resolution SM recovery and shorten its acquisition time, we propose a frequencydomain structure consistency loss function and data component embedding strategy to model global and local structural information of SM. We adopt a transformer-based network to evaluate this function and the strategy. We evaluate our methods and state-of-the-art (SOTA) methods on the two simulation datasets and four public measured SMs in Open MPI Data. The results show that our method outperforms the SOTA methods in high-frequency structural signal recovery. Additionally, our method can recover a high-resolution SM with clear high-frequency structure based on a down-sampling factor of 16 less than 15 seconds, which accelerates the acquisition time over 60 times faster than the measurement-based HR SM with the minimum error (nRMSE=0.041). Moreover, our method is applied in our three in-house MPI systems, and boost their performance for signal *** Codes F.2.2 Copyright © 2025, The Authors. All rights reserved.

关键词： Signal reconstruction

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U-HRMLP: Refining Segmentation Boundaries in Histopathology Images

U-HRMLP: Refining Segmentation Boundaries in Histopathology ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Tao Wang Kai Zhang Weijie Wang Mingrui Ma Ye Zhang He Zhao Guixia Liu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China School of Information Science and Engineering Shandong Normal University China Department of Information Engineering and Computer Science University of Trento Italy Faculty of Information Engineering and Automation Kunming University of Science and Technology China School of Information Science and Technology Northeast Normal University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Segmenting regions of interest in histopathology images is a critical prerequisite for biomedical applications. CNN-based models have advanced medical image segmentation, yet accurately delineating boundaries is still challenging. This challenge arises from the loss of spatial resolution due to downsampling and the complexity of capturing contextual information when upsampling to the original resolution. To address these issues, we design U-HRMLP, a U-shaped network based on a hybrid combination of HRNet and Multi-Layer Perceptron (MLP). Specifically, we propose a multi-scale feature fusion module to avoid the loss of image resolution in the encoding stage as much as possible. Simultaneously, we propose an MLP-based decoding block to recover image details and resolution progressively, alleviating the issues related to the inadequate segmentation of smaller objects and the blurring of tissue boundaries. Results on the MoNuSeg and GlaS datasets demonstrate that U-HRMLP outperforms conventional or transformer-based methods and achieves state-of- the-art performance.

关键词： Measurement Image segmentation Histopathology Biological system modeling Semantics Refining Transformers

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Efficient Ensemble Broad Learning System Based on Dropout and Dropconnect

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The International Journal of Intelligent Control and Systems 2024年第2期29卷 79-87页

作者： Yiwan Cao Fei Chu Shuai Li C.L.Philip Chen Jun Fu School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110004China

Broad learning system(BLS)is an emerging neural network characterized by its rapid processing and robust generalization ***,determining the appropriate structure for broad learning system is also a *** addition,broad learning system may perform overfitting due to the dependence between nodes in processing fully connected *** deal with these problems,an efficient ensemble broad learning system based on Dropout and Dropconnect is proposed in this *** proposed Dropout ensemble broad learning system randomly discards hidden nodes to improve diversity between individuals and reduce the synergy between nodes to improve prediction *** Dropconnect ensemble broad learning system randomly drops connection weights to generate more complementary models by adding input attribute *** experimental results on the UCI datasets confirm that the method proposed in this paper can solve the problem of model overfitting caused by the strong dependence between the nodes of ensemble broad learning *** proposed algorithm outperforms the original BLS in terms of prediction stability and classification accuracy.

关键词： Broad learning system ensemble learning Dropout Dropconnect

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Gmnet: Low Overlap Point Cloud Registration Based on Graph Matching

SSRN

引用

SSRN 2024年

作者： Cao, Lijia Wang, Xueru Guo, Chuandong School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin644000 China School of Automation and Information Engineering Sichuan University of Science & Engineering Yibin644000 China Artificial Intelligence Key Laboratory of Sichuan Province Yibin644000 China Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things Yibin644000 China

The quality of point cloud registration highly depends on the accuracy of point correspondences. Although significant progress has been made in this area by most methods, they still perform poorly when handling low-overlap point clouds and often overlook the topological structure characteristics of dense points. To address these issues, we propose the graph matching network (GMNet). This network constructs graph features based on the dense point features obtained from the first point cloud sampling and the superpoints features encoded with geometry. In the dense point stage of local patches, we use intra-graph and cross-graph convolutions to extract deeper global information, aiming to achieve robust correspondences. The GMNet network significantly improves the quality of low-overlap point cloud registration, demonstrating high accuracy and robustness. Experimental results on public datasets for objects, indoor, and outdoor scenes validate the effectiveness of GMNet. Furthermore, on the low-overlap 3DLoMatch dataset, our registration recall rate remains stable at 72.6%, with the inlier ratio improving by up to 9.9%. © 2024, The Authors. All rights reserved.

关键词： Surface measurement

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model 42

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

引用

42nd Chinese Control Conference, CCC 2023

作者： Ao, Mengting Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789887581543

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Long short-term memory

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Cooperative Control Strategy of Dual Manipulators Based on Model Reference Adaptive Admittance Control

Cooperative Control Strategy of Dual Manipulators Based on M...

引用

automation, Control and Robotics engineering (CACRE), International Conference on

作者： Hongmin Wang Meng Jiang Longhua Wu Junlin Huang Zengxi Pan Dagang Li Yingying Song Hui Huang School of Mechanical and Automation Engineering Institute of Carbon Peaking and Carbon Neutralization Wuyi University Jiang Men China School of Mechanical and Automation Engineering Wuyi University Jiang Men China Robotics and Welding Automation Laboratory University of Wollongong Australia School of Computer Science and Engineering Macau University of Science and Technology Macau China Faculty of Innovation and Entrepreneurship Wuyi University Jiang Men China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

This study aims to solve the dual robotic arm collaboration problem by proposing a method based on an adaptive conductivity control algorithm for nondestructive grasping and handling of unknown manipulated objects by dual robotic arms. The method is capable of dynamically tracking the intended force of the unknown entity while maintaining the desired trajectory and compensating for the error in the state of the unknown object. In this paper, adopting a two-arm collaborative robotic arm motion planning method oriented to the manipulated object, the kinematics of a singular appendage is calculated by closed-loop constraints. The collaborative control strategy of the dual robotic arms for the operated object is established, which facilitating the pursuit of the intended force and the rectification of the positional path deviation induced by external interference. Through simulation and experimental verification, the method demonstrates satisfactory performance regarding the error in tracking position and the steady state deviation in force, offering a viable solution for attaining heightened precision in tracking both position and force.

关键词： Trajectory tracking Force Collaboration Kinematics Manipulators Trajectory Steady-state

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