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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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NMPC for Trajectory Tracking of Hybird Terrestrial-Aerial Vehicles with Collision Avoidance

NMPC for Trajectory Tracking of Hybird Terrestrial-Aerial Ve...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Haoyu Wang Zhiqiang Miao Haoming Tang Yizong Chen Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In recent years, researches on Hybrid Terrestrial-Aerial Vehicles (HTAVs) have received a lot of attention. However, most of the existing researches focus on achieving basic motion control in both modes, thus neglecting the situation of encountering obstacles during motion. To fill this research gap and achieve accurate trajectory tracking with collision avoidance, we proposes the use of Nonlinear Model Predictive control (NMPC). In this paper, we firstly focus on passive-wheeled HTAVs as the target platform. Subsequently, we introduce distance constraint function and dynamics models for both aerial and terrestrial modes, along with three motion constraints specific to the terrestrial mode. Then we designed the NMPC controller base on distance constraint function and the dynamics models for each mode, incorporating the motion constraint in the terrestrial controller to ensure smooth movement on the ground. At the end of this paper, we conduct simulation experiments to evaluate the effectiveness of trajectory tracking. The results of simulations demonstrate that regardless of the mode, the HTAV achieves a relatively high tracking accuracy and avoid collision when following a nonlinear trajectory with fixed initial position and orientation. Additionally, the position error converges rapidly and exhibits minimal fluctuation, highlighting the significant role played by the added constraints in control.

关键词： Adaptation models Accuracy Fluctuations Trajectory tracking Simulation Aerodynamics Trajectory Vehicle dynamics Collision avoidance robots

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Velocity-free Formation control for Quadrotors Using High-order Sliding Mode Differentiator

Velocity-free Formation Control for Quadrotors Using High-or...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Qiong Lin Zhiqiang Miao Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China

This paper deals with the problem of achieving formation control for underactuated multiple quadrotors without velocity measurements by employing the higher-order sliding mode (HOSM) differentiator. The primary objective of this work is to attain a coordinated formation behavior among the quadrotor group. In this paper, the dynamics of each quadrotor are transformed into a new form, wherein the attitude is described using rotation matrices. Subsequently, an intermediate guide is designed using the HOSM differentiator, taking into account the desired relative positions between the quadrotors and their actual positions. Next, the actual control thrusts and torques for each quadrotor are designed and solved by the intermediate guide. Finally, rigorous theoretical analysis and multiple simulations are performed to ensure that the multiple quadrotors converge asymptotically and accurately to the desired formation shape.

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Machine-learning-based detection for quantum hacking attacks on continuous-variable quantum-key-distribution systems

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Physical Review A 2023年第6期107卷 062422-062422页

作者： Chao Ding Shi Wang Yaonan Wang Zijie Wu Jingtao Sun Yiyu Mao College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha 410082 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China

Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.

关键词： Quantum cryptography

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Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Grasping in Dexterous robotics

arXiv

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arXiv 2024年

作者： Yang, Fan Chen, Wenrui Yang, Kailun Lin, Haoran Luo, DongSheng Tang, Conghui Li, Zhiyong Wang, Yaonan The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features of objects serve as a bridge in the functional interaction between agents and objects. However, leveraging these affordance cues to help robots achieve functional tool grasping remains unresolved. To address this, we propose a granularity-aware affordance feature extraction method for locating functional affordance areas and predicting dexterous coarse gestures. We study the intrinsic mechanisms of human tool use. On one hand, we use fine-grained affordance features of object-functional finger contact areas to locate functional affordance regions. On the other hand, we use highly activated coarse-grained affordance features in hand-object interaction regions to predict grasp gestures. Additionally, we introduce a model-based post-processing module that includes functional finger coordinate localization, finger-to-end coordinate transformation, and force feedback-based coarse-to-fine grasping. This forms a complete dexterous robotic functional grasping framework GAAF-Dex, which learns Granularity-Aware Affordances from human-object interaction for tool-based Functional grasping in Dexterous robotics. Unlike fully-supervised methods that require extensive data annotation, we employ a weakly supervised approach to extract relevant cues from exocentric (Exo) images of hand-object interactions to supervise feature extraction in egocentric (Ego) images. Correspondingly, we have constructed a small-scale dataset, FAH, which includes near 6K images of functional hand-object interaction Exo images and Ego images of 18 commonly used tools performing 6 tasks. Extensive experiments on the dataset demonstrate that our method outperforms state-of-the-art methods, and real-world localization and grasping experiments validate the practical applicability of our approach. The source code and the establishe

关键词： Extraction

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HNSRRT*:A Path Planning Algorithm Based On Heuristic Non-Uniform Sampling Method In Complex Obstacle Environment

HNSRRT*:A Path Planning Algorithm Based On Heuristic Non-Uni...

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Chinese control Conference (CCC)

作者： Zhiwen Xu Hui Zhang Bo Chen Xidong Zhou Songtao Yin Lian Yang College of Electrical and Information Engineering Changsha University of Science and Technology Hunan China College of Robotics and Robot Visual Perception and Control Technology National Engineering Research Center Hunan University Hunan China

The traditional sampling-based algorithm such as Rapidly Random-exploring Tree (RRT) and various varieties have achieved tremendous success in the area of path planning. However, their excessive exploration in the state space leads to long time to find the optimal solution, large memory usage and cannot guarantee the quality of the planned path(generally evaluated by the cost of search time and the length of path) in sophisticated space. In this article, we propose an optimal path planning algorithm based on heuristic non-uniform sampling, namely the HNSRRT*, which successfully plans path in complex obstacle environments with optimal length and minimum time cost. The HNSRRT* utilizes heuristic function to generate non-uniform sampling distribution by Gaussian distribution,and constraints on sampling points can reduce the time wasted and path length increase caused by excessive exploration. We test the proposed HNSRRT* in 2D and 3D complex obstacle environment,comparing it with the three traditional sampling-base algorithms. The simulation results indicated that the effectiveness and efficiency of HNSRRT* and have an obvious improvement in term of time cost, path length compared with the existing algorithms.

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Forecasting of the Incoming Dustcarts of a Waste Transfer Station Based on SARIMA Model

Forecasting of the Incoming Dustcarts of a Waste Transfer St...

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Chinese control and Decision Conference, CCDC

作者： Jiakang Zhou Qiu Fang Han Zhu Haojie Song Yaonan Wang National Engineering Reserch Center for Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai Chengtou Environment (Group) Co.

The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation, because it is helpful for the station management and the planning of resources. In this study, a seasonal autoregressive integrated moving average (SARIMA) model is suggested to forecast the incoming dustcarts of a waste transfer station. The dataset utilized contains both the hourly-sampled quantity and proportion of residual waste dustcarts. The outcomes of single step and multi-step forecasting are examined with different performance measures in order to confirm SARIMA’s effectiveness. The experimental results show that the SARIMA model has better prediction results compared with the LSTM model. In addition, SARIMA model has high accuracy in both single step and multi-step forecasting, but multi-step forecasting is more effective for solving real-world issues because of its less time-consuming.

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Self-Contrastive Learning Fault Diagnosis Network for Signal Imaging: A Case Study

Self-Contrastive Learning Fault Diagnosis Network for Signal...

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Chinese Automation Congress (CAC)

作者： Wenbin He Jianxu Mao Yaonan Wang Zhe Li Feng Yan Kexin Liu College of Electrical & Information Engineering and National Engineering Research Center of Robot Visual Perception & Control Technology Hunan University Hunan China College of Control Science & Engineering and State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The deep convolution method based on MSDP signal imaging has been proven to be an effective means of monitoring the robot grinding process. This method has very high requirements on the quality of imaging and requires a lot of computing resources. This paper proposes an image self-contrastive learning network that can effectively extract fault information from low-pixel fuzzy images and achieve efficient and accurate condition monitoring. The network first calculates the correlation distance matrix between two identical modified symmetrized dot pattern (MSDP) images and uses the generated similarity labels to calculate the contrast loss to extract the embedded features of the image. Finally, the classification network is trained using the cross entropy loss to achieve the classification of fault images. The method was verified on a robotic grinding platform to significantly reduce the calculation time while maintaining the recognition accuracy, which provides reliable technical support for the real-time monitoring of the robotic grinding process.

关键词： Fault diagnosis Vibrations Accuracy Image recognition Imaging Feature extraction Real-time systems robots Monitoring Image classification

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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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Bridging on-line systems modeling with fault detection for a class of unknown nonlinear distributed parameter systems

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IFAC-PapersOnLine 2022年第6期55卷 228-233页

作者： Yun Feng Yaonan Wang Yazhi Zhang College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha 410082 China College of Electric and Information Engineering Hunan Institute of Engineering Xiangtan 411104 China

Different from the traditional model-based fault diagnosis paradigm which is established upon the well-known observer design and analysis, a novel data-driven framework is proposed by combing systems modeling with fault detection for a class of 1-D unknown distributed parameter systems. The key idea is to transfer the on-line modeling error into the residual signal for fault detection. The proposed methodology only utilizes the I/O data and does not require extra knowledge of the system model, which increases its usability at large. Numerical simulations on a commonly used benchmark are presented for method validation.

关键词： AI FDI methods Neural approximations for optimal control estimation FDI for nonlinear Systems Distributed parameter systems

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