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IEEE International Conference on Industrial technology (ICIT)

作者： Yunlong Ma Yaonan Wang Yiming Jiang Xianen Zhou Ge Zhu Qing Zhu Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Xiangjiang Laboratory Hunan China College of Electrical and Information Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan University Changsha China ZTE Corporation Shenzhen China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In server board assembly tasks, the effect of vision-based robot assembly schemes is not ideal due to the small installation gap and the blocking of vision. Adding force sensors and force controllers can be a good solution to the above problems and increase the flexibility in the assembly process. In this paper, we propose a force control strategy applied to a server board assembly task. The method is divided into two main parts. In the first part, the zero offset of the force sensor and the load gravity are calibrated and compensated, so that the external force on the load is accurately obtained. In the second part, the admittance controller is designed to achieve compliant behavior between the robot and the environment. Finally, the experimental verification of the board insertion is carried out on the experimental platform. Experimental results verify the effectiveness and practicability of the proposed method.

关键词： Process control robot sensing systems Manipulators Servers Force sensors Admittance Force control

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An insulator feature-enhanced segmentation method based on the residual-type attention mechanism

An insulator feature-enhanced segmentation method based on t...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Xin Tang Jianxu Mao Junfei Yi Ziming Tao Zhenyu He College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Changsha China

ISBN: (纸本)9781665479691

As an crucial component of power line, the timely check for the state of insulator is necessary for the normal running of transmission lines. In view of debasement of insulator segmentation accuracy in current transmission line caused by complex background, low contrast, and the quality of images is not guaranteed, we improve U-Net by combining attention mechanism and residual connection. Residual connection is added to the encoder part to improve the extraction of low-level semantic information, and the attention mechanism is added to the decoder part for integrating high and low level features better and reduce the error between them. We confirm the effectiveness of the improved module through experiments, while the results showing that the improved U-Net model segmentation performance on insulator dataset is improved from 0.875 to 0.912. And our method also outperforms previous segmentation work on insulator dataset.

关键词： Image segmentation Power transmission lines Automation Semantics Insulators Feature extraction Information management

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Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Feature...

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IEEE International Conference on robotics and Biomimetics

作者： Ke Cao Ruiping Liu Ze Wang Kunyu Peng Jiaming Zhang Junwei Zheng Zhifeng Teng Kailun Yang Rainer Stiefelhagen Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to dynamic and challenging situations, such as poor lighting conditions and motion blur. To tackle this issue, we propose a tightly-coupled LiDAR-visual SLAM based on geometric features, which includes two sub-systems (LiDAR and monocular visual SLAM) and a fusion framework. The fusion framework associates the depth and semantics of the multi-modal geometric features to complement the visual line landmarks and to add direction optimization in Bundle Adjustment (BA). This further constrains visual odometry. On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features. It adjusts the direction of linear feature points and filters out outliers, leading to a higher accurate odometry system. Finally, we employ a module to detect the subsystem’s operation, providing the LiDAR subsystem’s output as a complementary trajectory to our system while visual subsystem tracking fails. The evaluation results on the public dataset M2DGR, gathered from ground robots across various indoor and outdoor scenarios, show that our system achieves more accurate and robust pose estimation compared to current state-of-the-art multi-modal methods.

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An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field

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Computer Modeling in engineering & Sciences 2020年第7期124卷 129-149页

作者： Qin Wan Xiaolin Zhu Yueping Xiao Jine Yan Guoquan Chen Mingui Sun College of Electric and Information Engineering Hunan Institute of EngineeringXiangtan411104China National Engineering Research Laboratory for Robot Vision Perception and Control Hunan UniversityChangshaChina The Laboratory for Computational Neuroscience University of PittsburghPittsburghPA 15260USA

Detecting moving objects in the stationary background is an important problem in visual surveillance ***,the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic *** this paper,according to the basic steps of the background subtraction method,a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the Markov random ***,the contributions are as follows:1)A new nonparametric strategy is utilized to model the background,based on an improved kernel density estimation;this approach uses an adaptive bandwidth,and the fused features combine the colours,gradients and positions.2)A Markov random field method based on this adaptive background model via the constraint of the spatial context is proposed to extract objects.3)The posterior function is maximized efficiently by using an improved ant colony system *** experiments show that the proposed method demonstrates a better performance than many existing state-of-the-art methods.

关键词： Object detection non-parametric method markov random field

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Power Grid Fastener Defect Detection Based on Dual Context Information Fusion

Power Grid Fastener Defect Detection Based on Dual Context I...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Liu Qingxian Mao Jianxu Yi Junfei Tao Ziming Yu Junlong College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Changsha China

ISBN: (纸本)9781665479691

The working state of fasteners is closely related to the safe and stable operation of the power grid. However, the performance of the existing fastener defect detection models is not enough to meet the requirements, because the size of object is small, the environment is complex, the sample size is unbalanced and so on. Therefore, we design a simple but effective feature fusion method, called Double Context Information Enhancement Module. The model can fully learn the features of fasteners by expanding the receptive field of the feature map during the construction of the feature pyramid, and the feature expression ability of fasteners can be enhanced by learning the features of adjacent layers after the construction of the feature pyramid. Through experiments, we proved that DCEM can improve about 8.8AP compared with the original model, and at the same time, the effectiveness and advancement of DCEM are proved by comparing with SOTA.

关键词： Correlation Automation Aggregates Fasteners Feature extraction Power grids Information management

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Binocular vision System for Measuring the Height of control Rod Drive Rod in Nuclear Reactor

Binocular Vision System for Measuring the Height of Control ...

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Chinese control Conference (CCC)

作者： Xianen Zhou Yingjian Wen Qing Zhu Yaonan Wang Feixiang Sun Shaonan Chen National Engineering Research Center for Robot Vision Perception and Control Technology Hunan University Jiangxi Communication Terminal Industry Technology Research Institute Co. Ltd. University of Electronic Science and Technology of China China Nuclear Power Technology Research Institute Co. Ltd.

Nuclear energy is widely recognized as an important source to meet the increasing energy demand in the future. However, since the occurrence of the Fukushima accident, its potential safety hazards have been strongly concerned by the public, and has become one of the most significant problems to be solved at this stage. In this paper, a vision inspection system with support detection scheme based on RGB-D fusion is proposed to achieve accurate detection of the control rod position for the potential danger in the decoupling process of the control rod drive mechanism. In order to separate the depth region containing the relevant information of the driving rod head, the ellipse region of the top ring of the driving rod is detected from the color image by the method based on line segment extraction and region of interest detection. Due to the smoothness and reflection of the surface of the control rod, part of the depth of the head of the control rod is lost. In order to predict the reference distance between the top value of the control rod and the camera, a center point filling method based on multi-direction linear regression in gradient stage is designed. In order to evaluate the effectiveness of the proposed method, a series of experiments are carried out on the head location and depth calculation of the drive rod. The results show that the vision inspection system in this paper can achieve high-precision positioning of the control rod.

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Semi-supervised Cloud Edge Collaborative Power Transmission Line Insulator Anomaly Detection Framework 11th

Semi-supervised Cloud Edge Collaborative Power Transmission ...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Yang, Yanqing Mao, Jianxu Zhang, Hui Chen, Yurong Zhong, Hang Huang, Zhihong Wang, Yaonan National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Hunan Changsha China State Grid Hunan Electric Power Corporation Limited Research Institute Hunan Changsha China

ISBN: (纸本)9783030873547

The widely deployed power transmission line expedites developing the age of electricity. Thus, it is necessary to maintain a power system with a great quantity of manpower and material resources, especially for crucial equipment, such as insulator string. However, the current main inspection method relies on artificial with the problem of time-consuming and labor-intensive. There is a trend of utilizing deep learning techniques on unmanned aerial vehicles (UAVs) to accomplish the inspection task, but its development is restricted by the limitation of energy. In this paper, we propose a semi-supervised cloud edge collaborative insulator string anomaly detection framework. Specifically, an anchor-free object detector is deployed on the edge device for locating the insulator. On the cloud side, we propose a generative insulator defect detection model based on the autoencoder (AE) with a generator-discriminator pattern. Particularly, we introduce the variational memory encoder-decoder architecture to model defect-free insulator data distribution. Furthermore, the adversarial strategy is employed to regularize the generated data space with input data space. In the end, the anomaly can be detected if its data space is an outlier of training defect-free distribution. Comprehensive experiments demonstrate that our method can effectively reduce the computational load, meanwhile archiving superior performance, including accuracy (0.968) and recall (0.985), for defect recognition using a standard insulator data set. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Resilient Formation control Based on Watermarks for Networked Quadrotors under Deception Attacks

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IEEE Transactions on Vehicular technology 2025年

作者： Zhan, Weiwei Miao, Zhiqiang Zeng, Jianxin Chen, Yanjie Wu, Zheng-Guang He, Wei Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China Fuzhou University School of Mechanical Engineering and Automation Fuzhou350108 China National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China University of Science and Technology Beijing Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology Institute of Artificial Intelligence Beijing100083 China

This article addresses the problem of formation control of networked UAVs under deception attacks. A lightweight resilient formation control framework based on watermarks is proposed to achieve the desired formation configuration without extra monitoring attack status. Since deception attacks are covert, the shared information is watermarked before transmission to establish the protocol among networked UAVs. To facilitate the resilient formation controller design, a decoder is first developed to decipher the inverse transformation of deception attacks, where the watermark is the criterion for approximating the inverse transformation. The Gauss error function is introduced to eliminate some unrealistic input commands under deception attacks, obtaining the predefined input performance. Then, a resilient formation controller is developed to guarantee the global boundedness of all signals within the inner-loop and outerloop system, while the formation error converges to a small neighborhood of zero by solving the linear matrix inequality. Two challenging deception attack examples in simulations and experiments are conducted to verify the effectiveness and performance of the proposed resilient formation control framework. © 1967-2012 IEEE.

关键词： Inverse transforms

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Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

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9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

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Generative Adversarial Network with Separate Learning Rule for Image Generation

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Journal of Donghua University(English Edition) 2020年第2期37卷 121-129页

作者： YIN Feng CHEN Xinyu QIU Jie KANG Yongliang College of Automation and Electronic Information Xiangtan UniversityXiangtan 411105China National Engineering Laboratory of Robot Vision Perception and Control Technology Changsha 410012China

Boundary equilibrium generative adversarial networks(BEGANs)are the improved version of generative adversarial networks(GANs).In this paper,an improved BEGAN with a skip-connection technique in the generator and the discriminator is ***,an alternative time-scale update rule is adopted to balance the learning rate of the generator and the ***,the performance of the proposed method is quantitatively evaluated by Fréchet inception distance(FID)and inception score(IS).The test results show that the performance of the proposed method is better than that of the original BEGAN.

关键词： generative adversarial network(GAN) boundary equilibrium generative adversarial network(BEGAN) Fréchet inception distance(FID) inception score(IS)

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