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2nd International Conference on Artificial Intelligence, Systems and Network Security, AISNS 2024

作者： Wan, Qin Ning, Shunxing Xu, Pin Li, Zhi Hunan Institute of Engineering Hunan Xiangtan China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha China

ISBN: (纸本)9798400711237

This paper proposes a vision-based formation control method for multi-robot systems in the absence of inter-robot communication, employing a leader-follower scheme with a single Kinect camera as the sole sensor. By utilizing a pose estimation algorithm based on RGBD images, the relative position and orientation of the leader are captured. An anticipated formation shape aligned in the same movement direction is generated using a dipole vector field, ensuring formation stability. To address the limited field of view of the Kinect camera, an auxiliary system is designed to transform visibility constraints into input constraints, enabling effective control under non-communicative conditions. Simulation and experimental results demonstrate that the proposed method achieves real-time and robust formation control despite visibility limitations, simplifying the system architecture and enhancing practicality. © 2024 Copyright held by the owner/author(s).

关键词： Mobile robots

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Adaptive control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 148-158页

作者： Shouyan Chen Weitian He Zhijia Zhao Yun Feng Zhijie Liu Keum-Shik Hong School of Mechanical and Electrical Engineering Guangzhou University the School of Control Science and Engineering Shandong University IEEE the College of Electrical and Information Engineering Hunan University the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University the School of Intelligence Science and Technology University of Science and Technology Beijing (USTB) the Institute for Future the School of AutomationQingdao University the School of Mechanical Engineering Pusan National University

In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second,a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently,by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally,the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

关键词： Adaptive control flexible manipulator intermittent actuator faults inverse hysteresis dynamics vibration control

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design

arXiv

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arXiv 2025年

作者： Wu, Mingzhu Jiang, Jianan Li, Xinglin Deng, Hanhui Wu, Di National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha China

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely FedGAI, employing federated learning to aid in sketch design. FedGAI is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through FedGAI, designers can generate sketches that incorporate various designers’ styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations indicate that our FedGAI system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Frequency shift-based 3D shape measurement in the presence of strong inter-reflection

Frequency shift-based 3D shape measurement in the presence o...

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2024 International Conference on Optical and Photonic engineering, icOPEN 2024

作者： Miao, Huisi Liu, Wan Yan, Ziyu Xu, Weidong Zhang, Dongbo Zhang, Hui School of Automation and Electronic Information Xiangtan University Xiangtan411105 China Xiangtan Electric Manufacturing Co..Ltd. Xiangtan411105 China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

Traditional fringe projection profilometry (FPP) struggles with robust imaging and high-precision three-dimensional (3D) reconstruction in complex lighting environments with strong inter-reflection. This paper proposes a method combining frequency-shifted fringe projection and epipolar constraint to achieve high-precision 3D profilometry in such scenarios. First, environmental light interference is mitigated and the maximum fringe frequency is reduced through window function analysis (WFA) and a four-step phase-shifting (PS). Then, direct and indirect illumination are separated in the Fourier domain. Direct illumination positions are identified using threshold segmentation and epipolar geometry constraint, enhancing imaging robustness in strong inter-reflection contexts. Experimental results demonstrate that the method constructs direct and indirect illumination feature models, accurately obtaining direct illumination information and eliminating multi-path interference. Additionally, in tests with complex geometrical surfaces, imaging efficiency and robustness significantly improve, with effective point cloud data increasing by over 30% and planar accuracy improving by 50%. © 2025 SPIE.

关键词： Light interference

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DDH-Net:A Dual Dynamic Head Network for Transmission Line Inspection

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Yu, Junlong Mao, Jianxu Wang, Yaonan He, Zhenyu Yi, Junfei Tao, Ziming Zhang, Hui Wang, Shaoyuan Hunan University College of Electrical and Information Engineering Hunan Changsha410012 China Hunan University National Engineering Laboratory of Robot Visual Perception and Control Technology College of Robotics Hunan Changsha410082 China

Transmission lines are a critical component of an electrical grid, and ensuring their effective detection is of utmost importance. However, problems such as fine-grained identification, target occlusion, and background complexity in aerial transmission line images challenge existing transmission line detection methods. This paper presents a dual dynamic head network (DDH-Net) to address the aforementioned issues. This network incorporates a dual dynamic head (DDH) composed of a multifeature fusion module (MFFM) and deformable convolution. DDH leverages deformable convolution to enhance the network's focus on the target of interest. Subsequently, the MFFM enhances the feature representation capabilities for individual targets, leading to discriminative feature extraction and improving the network's accuracy in detecting similar targets. Furthermore, we introduce the wise area intersection over union (WAIoU) loss, which reduces the network's attention on detection boxes that contain only partial target content through the adaptive allocation of loss weights to anchor boxes to mitigate occlusion-induced interference. Additionally, we incorporate area as a constraint to further enhance the network's localization accuracy. Finally, extensive experiments were conducted on a power transmission line dataset. The results demonstrated the superiority of the proposed module over other state-of-the-art (SOTA) methods. © 1963-2012 IEEE.

关键词： Convolution

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One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes

arXiv

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arXiv 2025年

作者： Jia, Wanjun Yang, Fan Duan, Mengfei Chen, Xianchi Wang, Yinxi Jiang, Yiming Chen, Wenrui Yang, Kailun Li, Zhiyong School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

Deformable object manipulation in robotics presents significant challenges due to uncertainties in component properties, diverse configurations, visual interference, and ambiguous prompts. These factors complicate both perception and control tasks. To address these challenges, we propose a novel method for One-Shot Affordance Grounding of Deformable Objects (OS-AGDO) in egocentric organizing scenes, enabling robots to recognize previously unseen deformable objects with varying colors and shapes using minimal samples. Specifically, we first introduce the Deformable Object Semantic Enhancement Module (DefoSEM), which enhances hierarchical understanding of the internal structure and improves the ability to accurately identify local features, even under conditions of weak component information. Next, we propose the ORB-Enhanced Keypoint Fusion Module (OEKFM), which optimizes feature extraction of key components by leveraging geometric constraints and improves adaptability to diversity and visual interference. Additionally, we propose an instance-conditional prompt based on image data and task context, effectively mitigates the issue of region ambiguity caused by prompt words. To validate these methods, we construct a diverse real-world dataset, AGDDO15, which includes 15 common types of deformable objects and their associated organizational actions. Experimental results demonstrate that our approach significantly outperforms state-of-the-art methods, achieving improvements of 6.2%, 3.2%, and 2.9% in KLD, SIM, and NSS metrics, respectively, while exhibiting high generalization performance. Source code and benchmark dataset will be publicly available at https://***/Dikay1/OS-AGDO. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yin, Yufeng Liu, Xiaoyan Zhang, Zichao Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Innovation Institute of Industrial Design and Machine Intelligence Quanzhou-Hunan University Quanzhou362006 China

Multi-view stereo aims to recover the 3D model of a scene from a set of images. However, low-textured areas in the scene have always been a challenge in 3D reconstruction. In this work, we propose a segmentation-guided multi-scale anchor deformation patch multi-view stereo. Specifically, we use the Segment Anything Model to distinguish different instances in the scene, and propose an anchor deformation strategy guided by segmentation to adaptively generate a multi-scale anchor patch, so that the depth can be refined from coarse to fine scales. To propagate a better hypothesis for low-textured areas where photometric consistency is unreliable, we propose a non-local adaptive propagation scheme by using the segmented mask as a propagation domain. In order to reduce the interference of illumination on reconstruction completeness, we propose an outlier depth cost refinement guided by reliable points that improves the performance of depth estimation in unevenly illuminated areas. As a result, our method achieves state-of-the-art performance among traditional methods and exhibits better generalization capabilities on the ETH3D, Tanks and Temples, and DTU datasets. © 1991-2012 IEEE.

关键词： Image segmentation

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Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Sun, Jingtao Wang, Yaonan Wang, Danwei Hunan University College of Electrical and Information Engineering National Engineering Research Centre for Robot Visual Perception and Control Changsha410082 China Nanyang Technological University School of Electrical and Electronic Engineering SG 639798 Singapore

Tracking the object 6-DoF pose is crucial for various downstream robot tasks and real-world applications. In this paper, we investigate the real-world robot task of aerial vision guidance for aerial robotics manipulation, utilizing category-level 6-DoF pose tracking. Aerial conditions inevitably introduce special challenges, such as rapid viewpoint changes in pitch and roll and inter-frame differences. To support these challenges in task, we firstly introduce a robust category-level 6-DoF pose tracker (Robust6DoF). This tracker leverages shape and temporal prior knowledge to explore optimal inter-frame keypoint pairs, generated under a priori structural adaptive supervision in a coarse-to-fine manner. Notably, our Robust6DoF employs a Spatial-Temporal Augmentation module to deal with the problems of the inter-frame differences and intra-class shape variations through both temporal dynamic filtering and shape-similarity filtering. We further present a Pose-Aware Discrete Servo strategy (PAD-Servo), serving as a decoupling approach to implement the final aerial vision guidance task. It contains two servo action policies to better accommodate the structural properties of aerial robotics manipulation. Exhaustive experiments on four well-known public benchmarks demonstrate the superiority of our Robust6DoF. Real-world tests directly verify that our Robust6DoF along with PAD-Servo can be readily used in real-world aerial robotic applications. The project homepage is released at Robust6DoF. © 1979-2012 IEEE.

关键词： visual servoing

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An Improved Dual Neural Network Method Based on Levy Flight for Multi-robot Cooperative Area Coverage Search in 3D Unknown Environments

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Zhang, Fangfang Wang, Yongqi Wang, Wenhao Xin, Jianbin Peng, Jinzhu Wang, Yaonan Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471039 China National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha410082 China

The research on multi-robot collaborative search in unknown 3D environments, based on bio-inspired neural networks, holds significant value and ***, challenges arise in 3D environments, including excessive turning and vertical movement, as well as the potential for collisions between robots. In response, we propose an improved Glasius Bio-Inspired Neural Network(GBNN) that mitigates decision conflicts among robots and considers the impact of turning and vertical movement on robot decision-making. Furthermore, to address the issue of robots getting trapped in local deadlocks during the search process, we present a dual neural network algorithm based on Levy flights. In the method Dual Glasius Bio-Inspired Neural Network based on Levy Flight(LF-DUAL-GBNN) proposed in this paper, robots obtain random target points through Levy flights and are then guided by a dual neural network to navigate to the vicinity of the target points, thus breaking free from local deadlock states. Finally, we conducted simulation experiments to validate the algorithm's effectiveness. © 2016 IEEE.

关键词： Microrobots

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