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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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Task and Motion Planning for Manipulators Using Linear Temporal Logics

Task and Motion Planning for Manipulators Using Linear Tempo...

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

作者： Tailai Liu Zhiqiang Miao Guangyu Qin Wenjie Wu Lin Li 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)9798350372601

ISBN: (纸本)9798350372618

This paper deals with the problem of combining task and motion planning for a manipulator system. We propose a linear temporal logic (LTL) approach which follows the traditional hierarchical planning process, including task planning and motion planning. Firstly, the feasible network topology graph is obtained from the high-level task planning module, and the planning algorithm of search is used to obtain the task sequence. Secondly, the coordination module receives the task sequence to extract the manipulator’s action sequence to make planning suggestions. Furthermore, a lower level planning module is used to receive the suggestions and motion planning is performed using sampled planning algorithms to generate and output the motion trajectory. Finally, we verified the feasibility of our method through simulations and physical experiments. Our method has smoother trajectories and is about 10% more efficient than the traditional method of setting way points.

关键词： Network topology robot kinematics Computational modeling Manipulators Real-time systems Planning Trajectory

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Distributed Formation Tracking control of Quadrotors Using $\mathcal{L}_control$ Adaptive control and Differentiators

Distributed Formation Tracking Control of Quadrotors Using $...

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Asian control Conference

作者： Yang Hu Zhiqiang Miao Qiong Lin 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)9789887581598

ISBN: (纸本)9798331540845

This paper addresses the formation tracking control problem of quadrotor UAV swarms, considering the situation where the linear velocity of quadrotor UAVs cannot be directly measured during actual flight and is susceptible to external time-varying disturbances. A distributed robust formation control scheme is developed within a directed graph communication topology to conserve communication resources. In this scheme, controllers based on $\mathcal{L}_control$ adaptive control strategy and differ-entiator are designed for the position subsystem and attitude subsystem of each quadrotor, enhancing the system's stability, reliability, and safety performance (resistance to matched and unmatched disturbances). Numerical experiments validate the effectiveness and performance of the proposed formation control scheme.

关键词： Simulation Directed graphs Autonomous aerial vehicles Formation control Stability analysis Topology Safety

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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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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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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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