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42nd Chinese control Conference, CCC 2023

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

ISBN: (纸本)9789887581543

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. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Template matching

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Adaptive robust RBFNNs-based model estimator for a small quadrotor aircraft robot

Adaptive robust RBFNNs-based model estimator for a small qua...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Hang Zhong Shushuai Li Yaonan Wang Hongjian Liu College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology

ISBN: (纸本)9781509061914

This paper presents an on-line estimator that incorporates adaptive MIMO radical basis function neural networks (RBFNNs) for model identification of quadrotor unmanned aerial vehicles (UAVs). The inputs and outputs of quadrotor aircrafts can be obtained from dynamic models or real attitude and position sensors. The adaptive learning rate is employed in the gradient descent method for the update of the weights of RBFNNs, and Lyapunov approach guarantees the stability of the global convergence of the modeling errors. The Welsch functions are also employed as the error functions to get rid of the influence from the noise due to disturbances like wind gusts. Simulation results using robotics Toolbox for Matlab verify the effectiveness and robustness of the proposed estimator compared with results of traditional RBFNNs. Experiment results from real aircraft platform show that RBFNNs combining adaptive learning rate and Welsch error functions can approximate the overall system with high accuracy and robustness to disturbances.

关键词： Atmospheric modeling Aircraft Mathematical model Adaptation models Robustness robots MIMO

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Cleaning robot Operation Decision Based on Causal Reasoning and Attribute Learning*

Cleaning Robot Operation Decision Based on Causal Reasoning ...

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2020 IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Yapeng Li Dongbo Zhang Feng Yin Ying Zhang Institute of Automation and Electronic Information Xiangtan University Xiangtan China Institute of National Engineering Laboratory of Robot Vision Perception and Control Technology Xiangtan University Xiangtan China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In order to improve the operation ability of cleaning robots, this paper proposes a decision method for cleaning robot's operation mode. Firstly, we use the hierarchical expression ability of deep network to obtain the attributes of garbage such as state, shape, distribution, size and so on. Then the causal relationship between the attributes and the operation modes can be built by using joint learning of association attributes with depth network model and causal inference. Based on this, a fuzzy inference decision network is designed. With the help of causal analysis, the structure of the decision model is greatly simplified. Compared with conventional fuzzy neural networks, the total parameters of the model are reduced by 2 / 3. The method proposed in this paper imitates the way that human dispose of different types of garbage and has good interpretability. The experimental results verify the effectiveness of the proposed method.

关键词： Training Shape Semantics Fuzzy neural networks Cleaning Cognition robots

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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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High Dynamic Range 3d Measurement with Adaptive Projection Pattern Generated from Saturated Image

SSRN

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SSRN 2024年

作者： Yu, Hongshan Liang, Xianqiang Yu, Ruiming Xu, Haiqiang Sun, Wei National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Changsha China

Fringe projection profilometry (FPP) shows the advantages of high efficiency and high accuracy in distance sensing. However, it is prone to measurement errors due to fringe saturation in high dynamic range (HDR) scenes. Popular adjust projection intensity (API) based FPP methods have been proposed to reduce the influence of reflectivity variance. Because existing API based FPP method depend on unsaturated images to generate adaptive projection patterns, its measurement time is increased. In this paper, we propose a high dynamic range 3D measurement method with adaptive projection pattern generated from saturated image. Firstly, we establish the reflectance relationship between unsaturated region and saturated region to compute adaptive projection intensity, which removes the dependence of unsaturated images. Secondly, we improve measurement efficiency in computation process by recycling the captured images. In addition, we propose a capture intensity correction method to remove the effect of light propagation distance. Experiments prove that the proposed method has significant advantages in a variety of HDR scenes. Compared with linear-API method, proposed method improves the measurement accuracy by 15.59%. Meanwhile, the number of projection patterns is reduced by 40% compared to the method which based on multiple exposure fusion image (MEFI). © 2024, The Authors. All rights reserved.

关键词： Reflection

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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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Single-Leader-Dual-Follower Teleoperation in Object-Holding Task with Internal Force Regulation 26

Single-Leader-Dual-Follower Teleoperation in Object-Holding ...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Huang, Darong Jiang, Yiming Yang, Chenguang South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Changsha410082 China University of the West of England Bristol Robotics Laboratory BristolBS16 1QY United Kingdom

ISBN: (纸本)9781860435577

robot teleoperation attracts growing attention of researchers in many domains. Plenty of factors contribute to the good performance of a smart teleoperation system, and one crucial factor is that it provides an environmental feedback for the operator. Hence, in this paper, we offer a solution to help operator to perceive the contact force of the tele-robot. In addition, we introduce a strategy to cooperatively teleoperate two follower robots by using only single leader1 robot, which helps to reduce the control burden for the operator, as well as enables the teleoperation system to avoid the problem caused by the mismatched degree of freedom (DoF). Moreover, an impedance model based technique is employed to regulate the holding internal force during object-holding tasks. Experiments are conducted gradually, using a Touch X device as the leader robot and two arms of the Baxter robot as the tele-robots. Experimental results validate the feasibility of the proposed teleoperation system. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Remote control

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Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

Multi-Expert Adversarial Attack Detection in Person Re-ident...

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International Conference on Computer vision (ICCV)

作者： Xueping Wang Shasha Li Min Liu Yaonan Wang Amit K. Roy-Chowdhury College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology China University of California

ISBN: (纸本)9781665428132

The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%).

关键词： Deep learning Computer vision Perturbation methods Neural networks Detectors Feature extraction Context modeling

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Sparse Optical Flow-Based Line Feature Tracking

arXiv

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arXiv 2022年

作者： Fu, Qiang Yu, Hongshan Ali, Islam Zhang, Hong National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University China Department of Computing Science University of Alberta Canada

In this paper we propose a novel sparse optical flow (SOF)-based line feature tracking method for the camera pose estimation problem. This method is inspired by the point-based SOF algorithm and developed based on an observation that two adjacent images in time-varying image sequences satisfy brightness invariant. Based on this observation, we redefine the goal of line feature tracking: track two endpoints of a line feature instead of the entire line based on gray value matching instead of descriptor matching. To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints;next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function;finally, connect the two tracked endpoints to generate a new line feature. The correspondence is established between the given and the new line feature. Compared with current descriptor-based methods, our TET method needs not to compute descriptors and detect line features repeatedly. Naturally, it has an obvious advantage over computation. Experiments in several public benchmark datasets show our method yields highly competitive accuracy with an obvious advantage over speed. The source code of our method is released at: https://***/cnqiangfu/TET. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features

arXiv

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arXiv 2020年

作者： Fu, Qiang Wang, Jialong Yu, Hongshan Ali, Islam Guo, Feng He, Yijia Zhang, Hong National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University China Department of Computing Science University of Alberta Canada Megvii Technology Beijing China

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time and high-efficiency optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono [1]. We observe that current works use the LSD [2] algorithm to extract line features;however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, we modify the LSD algorithm by studying a hidden parameter tuning and length rejection strategy. The modified LSD algorithm can run at least three times as fast as LSD. Further, by representing space lines with the Plücker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Plücker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16% less than that of VINS-Mono at the same pose update frequency. The source code of our method is available at: https://***/cnqiangfu/PL-VINS. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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