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

作者： Long, Yu Zhong, Hang Qian, Zhenyu Liu, Min Zhang, Hui Jiang, Yiming Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha China Hunan University College of Robotics Changsha410082 China China China Highway Engineering ConsultingCorporation DATA CO. LTD Beijing100089 China

ISBN: (纸本)9798350372601

In this article, an robust nonlinear observer-based visual servo adaptive control strategy is investigated for a multirotor for stable tracking of targets. This article uses the Newton Euler equation to model the dynamics of the multirotor and describes the visual servo system model. In order to avoid being affected by various disturbances, this article designs disturbance observer and velocity observer to improve the robustness of the system. Based on nonlinear observers, this article designs a layered controller that includes a visual outer loop controller and an adaptive sliding mode geometric inner loop controller. To verify the feasibility of this algorithm, this article conducts simulation experiments using MATLAB and Simulink, and adds constant disturbances for verification. Both simulation experiments and experimental results in real environments show that the controller we designs has excellent stability and robustness. © 2024 IEEE.

关键词： Euler equations

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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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Unsupervised Deep Homography Estimation based on Transformer

Unsupervised Deep Homography Estimation based on Transformer

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Tianjian Jiang Qiu Fang Qing Zhu Yaonan Wang Zhen Zhou Lin Chen Jiaming Zhou Yuefan Luo Chengzhong Wu National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha Hunan China Jiangxi Communication Terminal Industry Technology Research Institute.

Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features from image pairs and introduce learnable mask modules to improve homography estimation performance. However, they struggle to capture long-term dependencies between features and comprehend the global structures of image features. A deep unsupervised homography learning framework is proposed in this paper, consisting of a weight-sharing feature extraction network and a homography estimation network based on the Transformer model. The former extracts the local features of images, while the latter learns the correlation between them and understands the global features of images, enabling the algorithm to better estimate the homography of unaligned images. Experimental results demonstrate that the proposed method outperforms the advanced methods for estimating homography matrices in the CA-Unsupervised dataset.

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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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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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Improved YOLOv7 Based on Transformer for Object Detection in UAV-Captured Images

Improved YOLOv7 Based on Transformer for Object Detection in...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuefan Luo Qing Zhu Zhen Zhou Lin Chen Jiaming Zhou Tianjian Jiang Yijiang Li Danwei Wang Yaonan Wang National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha Hunan China School of Electrical and Electrical Engineering Nanyang Technological University Singapore Rep. of Singapore

As the drone captures image targets at different flying altitudes, their scales may vary significantly, which can pose challenges for the object detection model to accurately detect them. Additionally, tiny objects in the image contain minimal information, making them difficult to distinguish from the background. To overcome these two challenges, we proposed a network architecture that aims to improve the accuracy of tiny object detection in drone images. Specially, we designed a tiny object detector(TOD) that can effectively extract features of tiny objects and distinguish between tiny object features and image background. Furthermore, this TOD module contains a Convolutional visual Attention Network (CVAN) to better focus on the regions of tiny objects. Experimental results demonstrate that the proposed method achieves mAP@.5 accuracy of 53.9% on the VisDrone2021-test-dev dataset and improves by 2.8 % compared to YOLOv7.

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Multi-Agent Path Finding Using Imitation-Reinforcement Learning with Transformer

Multi-Agent Path Finding Using Imitation-Reinforcement Learn...

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IEEE International Conference on robotics and Biomimetics

作者： Lin Chen Yaonan Wang Zhiqiang Miao Yang Mo Mingtao Feng Zhen Zhou School of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Computer Science and Technology Xidian University Xian China

ISBN: (纸本)9781665481106

Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and reinforcement learning are applied to solve the MAPF problem and have achieved certain results, which provides a feasible solution for the path planning problem of large-scale robot systems. The current method improves the performance of distributed strategy-guided agent planning paths in complex environments by introducing the communication between graph neural networks and agents but dramatically reduces the system's robustness. This paper develops a novel imitation reinforcement learning framework by introducing Transformer, which enables algorithms to perform well in complex environments without relying on communication between agents. Compared with its counterparts, experiments show that the policy trained by our method guides the agent to drive from the initial position to the goal without collision and achieve better performance.

关键词： Learning systems robot kinematics Biomimetics Reinforcement learning Transformers Robustness Path planning

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