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Unsupervised Range-Nullspace Learning Prior for Multispectral Images Reconstruction

IEEE Transactions on Image Processing 2025年 34卷 2513-2528页

作者： Chen, Yurong Wang, Yaonan Zhang, Hui Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan Changsha410082 China

Snapshot Spectral Imaging (SSI) techniques, with the ability to capture both spectral and spatial information in a single exposure, have been found useful in a wide range of applications. SSI systems generally operate within the ‘encoding-decoding’ framework, leveraging the synergism of optical hardware and reconstruction algorithms. Typically, reconstructing desired spectral images from SSI measurements is an ill-posed and challenging problem. Existing studies utilize either model-based or deep learning-based methods, but both have their drawbacks. Model-based algorithms suffer from high computational costs, while supervised learning-based methods rely on large paired training data. In this paper, we propose a novel Unsupervised range-Nullspace learning (UnNull) prior for spectral image reconstruction. UnNull explicitly models the data via subspace decomposition, offering enhanced interpretability and generalization ability. Specifically, UnNull considers that the spectral images can be decomposed into the range and null subspaces. The features projected onto the range subspace are mainly low-frequency information, while features in the nullspace represent high-frequency information. Comprehensive multispectral demosaicing and reconstruction experiments demonstrate the superior performance of our proposed algorithm. © 1992-2012 IEEE.

关键词： Image reconstruction

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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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Key Technologies for Machine Vision for Picking robots:Review and Benchmarking

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Machine Intelligence Research 2025年第1期22卷 2-16页

作者： Xu Xiao Yiming Jiang Yaonan Wang College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan UniversityChangsha 410082China

The increase in precision agriculture has promoted the development of picking robot technology,and the visual recognition system at its core is crucial for improving the level of agricultural *** paper reviews the progress of visual recognition tech-nology for picking robots,including image capture technology,target detection algorithms,spatial positioning strategies and scene *** article begins with a description of the basic structure and function of the vision system of the picking robot and em-phasizes the importance of achieving high-efficiency and high-accuracy recognition in the natural agricultural ***-sequently,various image processing techniques and vision algorithms,including color image analysis,three-dimensional depth percep-tion,and automatic object recognition technology that integrates machine learning and deep learning algorithms,were *** the same time,the paper also highlights the challenges of existing technologies in dynamic lighting,occlusion problems,fruit maturity di-versity,and real-time processing *** paper further discusses multisensor information fusion technology and discusses methods for combining visual recognition with a robot control system to improve the accuracy and working rate of *** the same time,this paper also introduces innovative research,such as the application of convolutional neural networks(CNNs)for accurate fruit detection and the development of event-based vision systems to improve the response speed of the *** the end of this paper,the future development of visual recognition technology for picking robots is predicted,and new research trends are proposed,including the refinement of algorithms,hardware innovation,and the adaptability of technology to different agricultural *** purpose of this paper is to provide a comprehensive analysis of visual recognition technology for researchers and practitioners in the field of agricul-tural rob

关键词： Picking robot visual system perception technology image processing machine learning deep learning.

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Vision-Based Leader-Follower Formation control for Mobile robots 24

Vision-Based Leader-Follower Formation Control for Mobile Ro...

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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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Multi-Context Aggregation Network With Foreground Correction for Automated Few-Shot Defect Segmentation

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IEEE Transactions on Automation Science and engineering 2025年 22卷 13777-13787页

作者： Ma, Yunfeng Liu, Min Jiang, Shuai Wang, Xueping Bian, Yuan Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan Normal University College of Information Science and Engineering Hunan Changsha410082 China

State-of-the-art defect segmentation methods rely on sufficient training data and struggle to generalize to unseen categories. Few-Shot Semantic Segmentation (FSS) is introduced to specifically address these issues. However, existing FSS models still face two challenges in the industry. 1) Defects usually present as weak features, resulting in incomplete segmentation;2) Severe background interference often leads to incorrect segmentation. To tackle these problems, we propose the Multi-Context Aggregation Network (MCANet). Specifically, we design a Cross-Layer Multi-Level Feature Aggregation Module (CMAM). CMAM effectively aggregates discretely distributed multi-level defect features across different layers and guides the query image to perceive defects from the pixel level, which avoids incomplete segmentation caused by weak features. Additionally, a Foreground Correction Module (FCM) is developed, which is equipped with a dedicated background predictor (BP) and a foreground corrector (FC). BP places more emphasis on learning features from backgrounds rather than defects. FC achieves efficient feature ensemble and further suppresses the backgrounds misidentified as defects in CMAM. They collaborate to prevent incorrect segmentation caused by background interference. Extensive experiments demonstrate the effectiveness of our method. We achieve state-of-the-art results on both FSSD-12, a public benchmark FSS dataset for strip steel, and FSS-AEB, an FSS dataset for aero-engine blades. Specifically, with 1/5 support images, we achieve 64.6%/65.6% mIoU on FSSD-12 and 55.0%/57.8% mIoU on FSS-AEB. Note to Practitioners—Surface defect segmentation has always been a hot topic in the industry. However, existing methods rely on sufficient training data and struggle to generalize to unseen categories, which significantly hinders the automation of defect segmentation. To address this problem, we propose MCANet for automated few-shot defect segmentation. It achieves effective seg

关键词： Semantic Segmentation

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Path Integral Policy Improvement and Dynamic Movement Primitives Fusion-Based Impedance Force control With Error Loop Correction

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15034-15044页

作者： Liu, Mujie Chen, Haifei Li, Lijun Ma, Zhiqiang Xu, Yong Zhang, Hui Central South University of Forestry and Technology National Forestry and Grassland Engineering Technology Research Center for Harvesting Equipment of Nonwood Forest Fruits Engineering Research Center for Forestry Equipment of Hunan Province School of Mechanical and Intelligent Manufacturing Changsha410004 China Northwestern Polytechnical University Research Center for Intelligent Robotics School of Astronautics Xi’an710072 China Beijing Institute of Technology School of Automation Beijing100081 China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Changsha410082 China

Path Integral Strategy Improvement(PI2)-based impedance control is a superior scheme for preventing damage to the physical structure of the fruit during the harvesting process. However, it is highly sensitive to disturbances and has limited generalization ability during the parameter learning process, making it difficult to apply the correct gripping force to fruits with uncertain stiffness. To solve this problem, this paper proposes a variable impedance force control method that integrates PI2 with Dynamic Movement Primitives (DMPs), supplemented by a force error correction loop. Firstly, an adaptive impedance parameter matching mechanism based on gain schedules is designed to facilitate dynamic estimation of impedance parameters and enable precise force control. To further accelerate impedance parameter matching in unknown environments, the PI2 algorithm is introduced to optimize gain schedules, and DMPs are integrated to suppress disturbances, thereby improving the generalization ability of the impedance model’s parameter learning. In addition, an additional force error control loop has been designed to minimize the deviation between the desired and actual gripping force. Finally, the effectiveness of the proposed method is verified through simulation and experiment in fruit teleoperation picking robot. In the local-remote heterogeneous fruit teleoperation picking robot system, preventing fruit damage is critical to improving harvesting efficiency and quality. However, the existing PI2 algorithm suffers from limited generalization ability during the parameter learning process and is highly susceptible to environmental disturbances, presenting challenges in force control. To address this issue, this study proposes integrating DMPs into PI2 to optimize gain schedules and designing a force error control loop to refine trajectories, thereby achieving the force control objectives and effectively compensating for the shortcomings of PI2. Simulation and experimental res

关键词： Rotation

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E-3DGS: 3D Gaussian splatting with exposure and motion events

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Applied Optics 2025年第14期64卷 3897-3908页

作者： Yin, Xiaoting Shi, Hao Bao, Yuhan Bing, Zhenshan Liao, Yiyi Yang, Kailun Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China The Chair of Robotics AI and Real-Time Systems Technical University of Munich Munich Germany College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China

Achieving 3D reconstruction from images captured under optimal conditions has been extensively studied in the vision and imaging fields. However, in real-world scenarios, challenges such as motion blur and insufficient illumination often limit the performance of standard frame-based cameras in delivering high-quality images. To address these limitations, we incorporate a transmittance adjustment device at the hardware level, enabling event cameras to capture both motion and exposure events for diverse 3D reconstruction scenarios. Motion events (triggered by camera or object movement) are collected in fast-motion scenarios when the device is inactive, while exposure events (generated through controlled camera exposure) are captured during slower motion to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian splatting (3DGS). Our framework supports three modes: high-quality reconstruction using exposure events, fast reconstruction relying on motion events, and balanced hybrid optimizing with initial exposure events followed by high-speed motion events. On the EventNeRF dataset, we demonstrate that exposure events significantly improve fine detail reconstruction compared to motion events and outperform frame-based cameras under challenging conditions such as low illumination and overexposure. Furthermore, we introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method achieves faster and higher-quality reconstruction than event-based NeRF and is more cost-effective than methods combining event and RGB data. By combining motion and exposure events, E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset are available on GitHub. © 2025 Optica Publishing Group.

关键词： Exposure controls

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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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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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Prior Distribution and perception Radius Are Important: Learning 3-D Object Detector for Autonomous Driving

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Tao, Ziming Mao, Jianxu Peng, Weixing Wang, Yaonan Yi, Junfei Zhang, Hui Hunan University National Engineering Laboratory of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Robotics Hunan Changsha410082 China

This article proposes an efficient 3-D object detection algorithm for LiDAR point clouds in the field of autonomous driving. Currently, despite significant performance improvements in 3-D LiDAR object detection methods, there are still problems that have impact on 3-D detection accuracy. Typically, since the sampling process and grouping process introduced background points, the final regression step in the detection task would be greatly affected. To address this problem, a method named PR-SSD is proposed to retain more important foreground points during the training pipeline, improving detection accuracy. Specifically, the proposed approach utilizes prior information to guide semantic sampling, referred to as prior distributed semantic sampling (PDSS). This mechanism encourages the network to prioritize foreground targets in regression. In addition, a module named radius-aware attention multiscale grouping (RAMSG) is designed to dynamically reassign grouping weights for multiscale features. In other words, the network has adaptive detection capabilities for targets of different scales. Furthermore, PR-SSD is a single-stage detector which can be trained end-to-end. Finally, extensive experiments and evaluations on large-scale benchmarks KITTI demonstrate that our proposed method significantly enhanced 3-D object detector. In KITTI testing set, PR-SSD achieves 89.69%, 45.08%, and 80.01% detection performance for cars, pedestrians, and cyclists. © 1963-2012 IEEE.

关键词： Vehicle detection

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