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

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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Unsupervised Range-Nullspace Learning Prior for Multispectral Images Reconstruction

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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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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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Anti-Interference High-Speed Modulation Decoder for Quantum Key Distribution

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Chinese Physics Letters 2025年第1期42卷 34-39页

作者： Hua-Xing Xu Shao-Hua Wang Chang-Lei Wang Ping Zhang National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academic of Electronics and Information TechnologyBeijing 100041China CETC Academy of Electronics and Information Technology Group Co. Ltd.Beijing 100041China

Quantum key distribution is increasingly transitioning toward network applications,necessitating advancements in system performance,including photonic integration for compact designs,enhanced stability against environmental disturbances,higher key rates,and improved *** this letter,we propose an orthogonal polarization exchange reflector Michelson interferometer model to address quantum channel disturbances caused by environmental *** on this model,we designed a Sagnac reflector-Michelson interferometer decoder and verified its performance through an interference *** interference fringe visibility exceeded 98%across all four coding phases at 625 *** results indicate that the decoder effectively mitigates environmental interference while supporting high-speed modulation *** addition,the proposed anti-interference decoder,which does not rely on magneto-optical devices,is well-suited for photonic integration,aligning with the development trajectory for next-generation quantum communication devices.

关键词： decoder interferometer polarization

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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 and 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. © 2025 IEEE.

关键词： Rotation

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TIPS:Tailored Information Extraction in Public Security Using Domain-Enhanced Large Language Model

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Computers, Materials & Continua 2025年第5期83卷 2555-2572页

作者： Yue Liu Qinglang Guo Chunyao Yang Yong Liao School of Cyber Science and Technology University of Science and Technology of ChinaHefei230026China National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information TechnologyBeijing100041China

Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information *** data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit ***,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning *** address these,we propose *** collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and *** then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source *** showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error *** corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.

关键词： Public security information extraction large language model prompt engineering

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

arXiv

引用

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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