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arXiv 2023年

作者： Lin, Jiacheng Chen, Jiajun Yang, Kailun Roitberg, Alina Li, Siyu Li, Zhiyong Li, Shutao The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Artificial Intelligence The University of Stuttgart Stuttgart70569 Germany The College of Electrical and Information Engineering The Key Laboratory of Visual Perception and Artificial Intelligence of Hunan Province Hunan University Changsha410082 China

Interactive Image Segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, but the critical issue of interaction ambiguity, notably hindering segmentation quality, has been under-researched. To address this, we introduce ADAPTIVECLICK – a click-aware transformer incorporating an adaptive focal loss that tackles annotation inconsistencies with tools for mask- and pixel-level ambiguity resolution. To the best of our knowledge, AdaptiveClick is the first transformer-based, mask-adaptive segmentation framework for IIS. The key ingredient of our method is the Click-Aware Mask-adaptive transformer Decoder (CAMD), which enhances the interaction between click and image features. Additionally, AdaptiveClick enables pixel-adaptive differentiation of hard and easy samples in the decision space, independent of their varying distributions. This is primarily achieved by optimizing a generalized Adaptive Focal Loss (AFL) with a theoretical guarantee, where two adaptive coefficients control the ratio of gradient values for hard and easy pixels. Our analysis reveals that the commonly used Focal and BCE losses can be considered special cases of the proposed AFL. With a plain ViT backbone, extensive experimental results on nine datasets demonstrate the superiority of AdaptiveClick compared to state-of-the-art methods. The source code is publicly available at https://***/lab206/AdaptiveClick. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

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Dynamic Hand Gesture Recognition via Electromyographic Signal Based on Convolutional Neural Network

Dynamic Hand Gesture Recognition via Electromyographic Signa...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shouan Song Lei Yang Man Wu Yanhong Liu Hongnian Yu School of Electrical Engineering Zhengzhou University China Robot Perception and Control Engineering Laboratory of Henan Province China Beijing Aerospace Control Center Beijing China Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (纸本)9781665442084

Dynamic gesture recognition is a typical human-computer interaction method owing to its great potential in practical applications. Currently, most of research work on gesture recognition has mainly focused on vision-based and surface electromyography (sEMG) methods. Compared to vision-based methods, the sequential sEMG signal can directly depict the muscle activity of different gestures which could lead to higher recognition efficiency. However, the effective feature design and selection of sEMG signal is still complicated since muscle fatigue and small electrode displacement will affect the recognition precision of sEMG signals. In this paper, a novel end-to-end dynamic gesture recognition method is developed. The raw sEMG signals are converted into an image form by using the time-frequency transformation method to obtain more comprehensive information for model training and test. And a recognition model based on Convolutional Neural Network (CNN) model is built for high-precision time-frequency image recognition. Experiments indicate that the proposed method could acquire distinguishing features from the pre-prossed images and the overall recognition accuracy on different gestures can reach up to 98.3%.

关键词： Training Time-frequency analysis Image recognition Continuous wavelet transforms Neural networks Gesture recognition Muscles

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PanoVpr: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows Across the Panoramic View

PanoVpr: Towards Unified Perspective-to-Equirectangular Visu...

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International Conference on Intelligent Transportation

作者： Ze Shi Hao Shi Kailun Yang Zhe Yin Yining Lin Kaiwei Wang State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China Shanghai SUPREMIND Technology Co. Ltd Shanghai China National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha China

Visual place recognition has gained significant attention in recent years as a crucial technology in autonomous driving and robotics. Currently, the two main approaches are the perspective view retrieval (P2P) paradigm and the equirectangular image retrieval (E2E) paradigm. However, it is practical and natural to assume that users only have consumer-grade pinhole cameras to obtain query perspective images and retrieve them in panoramic database images from map providers. To address this, we propose PanoVPR, a perspective-to-equirectangular (P2E) visual place recognition framework that employs sliding windows to eliminate feature truncation caused by hard cropping. Specifically, PanoVPR slides windows over the entire equirectangular image and computes feature descriptors for each window, which are then compared to determine place similarity. Notably, our unified framework enables direct transfer of the backbone from P2P methods without any modification, supporting not only CNNs but also Transformers. To facilitate training and evaluation, we derive the Pitts250k-P2E dataset from the Pitts250k and establish YQ360, latter is the first P2E visual place recognition dataset collected by a mobile robot platform aiming to simulate real-world task scenarios better. Extensive experiments demonstrate that PanoVPR achieves state-of-the-art performance and obtains 3.8% and 8.0% performance gain on Pitts250k-P2E and YQ360 compared to the previous best method, respectively. Code and datasets will be publicly available at PanoVPR.

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Generative Adversarial Network with Separate Learning Rule for Image Generation

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Journal of Donghua University(English Edition) 2020年第2期37卷 121-129页

作者： YIN Feng CHEN Xinyu QIU Jie KANG Yongliang College of Automation and Electronic Information Xiangtan UniversityXiangtan 411105China National Engineering Laboratory of Robot Vision Perception and Control Technology Changsha 410012China

Boundary equilibrium generative adversarial networks(BEGANs)are the improved version of generative adversarial networks(GANs).In this paper,an improved BEGAN with a skip-connection technique in the generator and the discriminator is ***,an alternative time-scale update rule is adopted to balance the learning rate of the generator and the ***,the performance of the proposed method is quantitatively evaluated by Fréchet inception distance(FID)and inception score(IS).The test results show that the performance of the proposed method is better than that of the original BEGAN.

关键词： generative adversarial network(GAN) boundary equilibrium generative adversarial network(BEGAN) Fréchet inception distance(FID) inception score(IS)

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Admittance Based robot Force control Framework for Server Board Assembly

Admittance Based Robot Force Control Framework for Server Bo...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yunlong Ma Yaonan Wang Yiming Jiang Xianen Zhou Ge Zhu Qing Zhu Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Xiangjiang Laboratory Hunan China College of Electrical and Information Engineering Hunan University Changsha China School of Robotics Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Hunan University Changsha China ZTE Corporation Shenzhen China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In server board assembly tasks, the effect of vision-based robot assembly schemes is not ideal due to the small installation gap and the blocking of vision. Adding force sensors and force controllers can be a good solution to the above problems and increase the flexibility in the assembly process. In this paper, we propose a force control strategy applied to a server board assembly task. The method is divided into two main parts. In the first part, the zero offset of the force sensor and the load gravity are calibrated and compensated, so that the external force on the load is accurately obtained. In the second part, the admittance controller is designed to achieve compliant behavior between the robot and the environment. Finally, the experimental verification of the board insertion is carried out on the experimental platform. Experimental results verify the effectiveness and practicability of the proposed method.

关键词： Process control robot sensing systems Manipulators Servers Force sensors Admittance Force control

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High-Speed Trajectory Tracking control for Quadrotors via Deep Reinforcement Learning

High-Speed Trajectory Tracking Control for Quadrotors via De...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Hean Hua Hang Zhong Hui Zhang Yongchun Fang Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Robotics Hunan University Changsha China Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper presents a learning-based high-speed trajectory tracking control strategy for quadrotors, which achieves efficient learning and strong reliability by the collaboration of deep reinforcement learning (RL) and self-tuning mechanism. Different from existing methods, the proposed strategy is designed to explore optimal control performance by taking advantage of model-based self-tuning mechanism and deep reinforcement learning. Specifically, the self-tuning guided deep RL scheme is put forward for quadrotors, with superior learning efficiency and strong adaptability. Firstly, a novel self-tuning mechanism is constructed and some auxiliary variables are introduced to enhance the tracking performance. Then, based on the model-driven self-tuning design, the deep RL is proposed to achieve model-guided learning, where the tuning actions are adopted in the evaluation process during training, aiming at removing the bad explorations by the carefully designed parallel evaluation. Finally, the convergence is analyzed based on the proposed learning framework, which indicates the efficient cooperation of exploration and self-tuning mechanism. To verify the effectiveness of the proposed controller, the guided training and hardware experiments are implemented to show efficient cooperation and satisfactory high-speed trajectory tracking control of the proposed method.

关键词： Training Adaptation models Trajectory tracking Heuristic algorithms Optimal control Deep reinforcement learning Hardware

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Dynamic Gesture Recognition Based on DS Evidence Theory

Dynamic Gesture Recognition Based on DS Evidence Theory

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第三十九届中国控制会议

作者： Haoxin Lei Yanhong Liu Lei Yang School of Electrical Engineering Zhengzhou University Robot Perception and Control Engineering Laboratory in Henan Province

Dynamic gesture recognition is gradually playing an increasingly important role in non-contact human-computer interaction. It is important to improve the accuracy of the recognition while reducing the computational cost. In this paper, we consider the dynamic gesture recognition based on multiple feature extraction and Dempster-Shafer(DS) evidence theory. First,to improve the accuracy of the gesture recognition, HOG2&HOG-LBP features are extracted and used for the training of Support Vector Machines(SVM). Then, DS evidence theory is used to optimize the classification results of SVMs. Experimental results on the Cambridge Hand Gesture Dataset demonstrate the effectiveness of the proposed method.

关键词： Dynamic gesture recognition Multiple feature extraction DS evidence theory SVM

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When Shannon Meets Nonstochastic Information: Communication and Estimation

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IEEE Transactions on Automatic control 2025年

作者： Zhang, Wentao Zuo, Zhiqiang Zhao, Rui Wang, Yijing Hu, Guoqiang Zhang, Hui Wang, Yaonan Hunan University School of Robotics Changsha410082 China Nanyang Technological University Continental-NTU Corporate Lab Singapore639798 Singapore Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore National Engineering Research Center for Robot Visual Perception and Control Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China

This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capacity of stationary memoryless multiple quasi-access channel under nonstochastic information theory. It is shown that the maximum information rate over such a channel is determined by a convex hull spanned by the zero-error capacities which are specified on the underlying sample spaces. Moreover, the state of a plant could be estimated provided that the maximal information rate is strictly greater than the topology entropy. © 1963-2012 IEEE.

关键词： Communication channels (information theory)

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Adaptive finite-time synchronized control of multi-robotic fiber placement system with model uncertainties and disturbances

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ISA Transactions 2025年

作者： Zhang, Ronghua Wang, Yaonan Xie, Wenfang Li, Pengcheng Tan, Haoran Jiang, Yiming College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China Department of Mechanical Industrial and Aerospace Concordia University MontrealQCH3G2W1 Canada College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China School of Robotics Hunan University Hunan Changsha410082 China National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Hunan Changsha410082 China

The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm. © 2025 Elsevier Ltd

关键词： Multipurpose robots

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Sparse Prior Is Not All You Need: When Differential Directionality Meets Saliency Coherence for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Zhou, Fei Fu, Maixia Qian, Yulei Yang, Jian Dai, Yimian Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou China Nanjing Marine Radar Institute Nanjing China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Infrared small target detection is crucial for the efficacy of infrared search and tracking systems. Current tensor decomposition methods emphasize representing small targets with sparsity but struggle to separate targets from complex backgrounds due to insufficient use of intrinsic directional information and reduced target visibility during decomposition. To address these challenges, this study introduces a Sparse Differential Directionality prior (SDD) framework. SDD leverages the distinct directional characteristics of targets to differentiate them from the background, applying mixed sparse constraints on the differential directional images and continuity difference matrix of the temporal component, both derived from Tucker decomposition. We further enhance target detectability with a saliency coherence strategy that intensifies target contrast against the background during hierarchical decomposition. A Proximal Alternating Minimization-based (PAM) algorithm efficiently solves our proposed model. Experimental results on several real-world datasets validate our method’s effectiveness, outperforming ten state-of-the-art methods in target detection and clutter suppression. Our code is available at https://***/GrokCV/SDD. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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