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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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Geometric-based Bimodal control for Hybrid Aerial/Ground Vehicles

Geometric-based Bimodal Control for Hybrid Aerial/Ground Veh...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Tang, Haoming Miao, Zhiqiang Wang, Haoyu Chen, Yizong Zhang, Hui Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Robotics Hunan University Changsha410082 China

ISBN: (纸本)9798350372601

Hybrid ground/aerial vehicles, capable of adapting to both terrestrial and aerial environments simultaneously, can accomplish more complex tasks, holding promising applications across various domains. Furthermore, designing a highly real-time multi-modal controller to adapt to environmental and modal changes, is currently a critical issue to be addressed. Based on this requirement, a geometric-based bimodal control structure is proposed in this study. We initially design the aerial controller in the Lie algebra space, based on a geometric attitude controller. Moreover, inspired by the characteristics of ground locomotion, we modify the aerial controller to satisfy the constraints of this mode. Through this approach, we can simplify the design process of the ground controller. Both controllers feature a similar and simpler structure, meet real-time control requirements, and allow them to use the same parameters. Ultimately, the proposed method's efficacy and performance are validated through ROS-Gazebo simulation environment. © 2024 IEEE.

关键词： Antenna grounds

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Tightly-Coupled LiDAR-visual SLAM Based on Geometric Features for Mobile Agents

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Feature...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Cao, Ke Liu, Ruiping Wang, Ze Peng, Kunyu Zhang, Jiaming Zheng, Junwei Teng, Zhifeng Yang, Kailun Stiefelhagen, Rainer Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Germany Hunan University School of Robotics China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology China

ISBN: (纸本)9798350325706

The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to dynamic and challenging situations, such as poor lighting conditions and motion blur. To tackle this issue, we propose a tightly-coupled LiDAR-visual SLAM based on geometric features, which includes two sub-systems (LiDAR and monocular visual SLAM) and a fusion framework. The fusion framework associates the depth and semantics of the multi-modal geometric features to complement the visual line landmarks and to add direction optimization in Bundle Adjustment (BA). This further constrains visual odometry. On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features. It adjusts the direction of linear feature points and filters out outliers, leading to a higher accurate odometry system. Finally, we employ a module to detect the subsystem's operation, providing the LiDAR subsystem's output as a complementary trajectory to our system while visual subsystem tracking fails. The evaluation results on the public dataset M2DGR, gathered from ground robots across various indoor and outdoor scenarios, show that our system achieves more accurate and robust pose estimation compared to current state-of-the-art multi-modal methods. © 2023 IEEE.

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Improved YOLOv7 Based on Transformer for Object Detection in UAV-Captured Images

Improved YOLOv7 Based on Transformer for Object Detection in...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Luo, Yuefan Zhu, Qing Zhou, Zhen Chen, Lin Zhou, Jiaming Jiang, Tianjian Li, Yijiang Wang, Danwei Wang, Yaonan Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China School of Electrical and Electrical Engineering Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798350337020

As the drone captures image targets at different flying altitudes, their scales may vary significantly, which can pose challenges for the object detection model to accurately detect them. Additionally, tiny objects in the image contain minimal information, making them difficult to distinguish from the background. To overcome these two challenges, we proposed a network architecture that aims to improve the accuracy of tiny object detection in drone images. Specially, we designed a tiny object detector(TOD) that can effectively extract features of tiny objects and distinguish between tiny object features and image background. Furthermore, this TOD module contains a Convolutional visual Attention Network (CVAN) to better focus on the regions of tiny objects. Experimental results demonstrate that the proposed method achieves mAP@.5 accuracy of 53.9% on the VisDrone2021-test-dev dataset and improves by 2.8 % compared to YOLOv7. © 2023 IEEE.

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Efficient Real-Time Recognition Model of Plant Diseases for Low-Power Consumption Platform

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2040-2054页

作者： Deng, Songyun Wu, Wanneng Zou, Kunlin Qin, Hai Cheng, Lekai Liang, Qiaokang Hunan University College of Electrical and Information Engineering Changsha410082 China Changsha University of Science and Technology School of Electrical and Information Engineering Changsha410114 China Hunan University National Engineering Research Center for Robot Vision Perception and Control College of Electrical and Information Engineering Changsha410082 China

Recognition and early warning of plant diseases is one of the keys to agricultural disaster prevention and mitigation. Deep learning-based image recognition methods give us a new idea for plant disease identification. Due to the harsh conditions in agricultural environment, recent research has focused on exploring ways to lightweight the recognition model for deployment on low-power devices. In this article, we propose an efficient and feature-guided real-time plant disease recognition model with a multiclassifier architecture, specifically designed for low-power devices. By comparing with other advanced methods, our model reaches the state of the art in the combined metrics of recognition accuracy, the number of parameters, and inference speed. First, we propose AMI-NanoNet based on Roofline theory to significantly reduce the number of parameters and computational complexity. This model can achieve 99.8343% accuracy on PlantVillage by using a feature-guided curriculum learning with stepwise training strategy. Moreover, we design another training strategy suitable for lightweight ensemble models. Based on this strategy, our model only needs to integrate the classifiers at the end of the network to achieve 99.8708% identification accuracy, and it hardly increases the number of operations and parameters of the network. Extensive evaluations on this dataset demonstrate the effectiveness of our ensemble learning method. Furthermore, we tested our proposed methods on another dataset from other domains to validate its applicability to different scenarios. Overall, our research provides a basis for rapid and intelligent identification of plant diseases. © 2023 IEEE.

关键词： Real time systems

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Dynamic Movement Primitives Based robot Skills Learning

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Machine Intelligence research 2023年第3期20卷 396-407页

作者： Ling-Huan Kong Wei He Wen-Shi Chen Hui Zhang Yao-Nan Wang School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China School of Robotics and the National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint *** DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion *** addition,motions are categorized into different goals and *** is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning *** experiment test on the Baxter robot verifies the effectiveness of the proposed method.

关键词： Dynamic movement primitives(DMPs) trajectory tracking control robot learning from demonstrations neural networks(NNs) adaptive control

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Adaptive Impedance control for Mobile Cooperative Manipulation Systems With Stability and Passivity Guarantees*

Adaptive Impedance Control for Mobile Cooperative Manipulati...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Danping Zeng Yaonan Wang Yiming Jiang Haoran Tan Zhiqiang Miao Yun Feng School of Robotics and the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha China College of Electrical and Information Engineering and the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

A number of robotic tasks exist, such as manipulating heavy and oversized objects, where a single manipulator is not enough and multiple mobile manipulators need to be used. When uncertainty exists, a compliant control strategy is expected to be applied for such a tightly coordinated multiple mobile manipulator system. An adaptive impedance control law that enables adaptive updating of impedance parameters is developed in this work. The proposed method ensures that all closed-loop systems boundedly and asymptotically realize the desired adaptive impedance dynamics. Furthermore, to ensure the passivity of the adaptive impedance system, the impedance system of each mobile manipulator is interconnected with a virtual energy tank, and the update of the impedance system parameters is guided according to the level of the energy to ensure that the adaptive impedance system is passive, thus realizing the adaptive trade-off between compliance and operational accuracy. Finally, a simulation for four mobile manipulators collaborating to carry an unknown object is provided to validate the effectiveness of the proposed one.

关键词： Adaptive systems Uncertainty Mechatronics robot kinematics Cooperative systems Stability analysis Trajectory Impedance Closed loop systems Manipulator dynamics

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An Improved SCA-BP Delay Prediction Method for Networked control Systems

An Improved SCA-BP Delay Prediction Method for Networked Con...

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2023 China Automation Congress, CAC 2023

作者： Xie, Jiayin Wang, Yaonan Tan, Haoran Wu, You Zhou, Bowen College of Electrical and Information Engineering Hunan University National Engineering Research Center for Robot Visual Perception and Control Changsha China Hunan Retoo Intelligent Technology Co. Ltd. Changsha China

ISBN: (纸本)9798350303759

A networked control system is a system that uses communication networks to connect the various parts. However, network delay affects the performance and stability of the networked control system. In order to eliminate the impact of network delay on the control system, an improved SCA-BP delay prediction method is proposed in this paper. The proposed method optimizes the initial weights and biases of the BP neural network, and further optimizes them through backpropagation gradient descent, obtaining the final prediction model. The proposed method introduces a random perturbation factor in the sine and cosine functions, which increases the explorability and diversity of the method. And they are sorted and grouped according to the fitness values of the candidate solutions. At the same time, different update strategies are adopted based on the different groups, enabling the method to perform global and local search simultaneously. In the update strategy, a linear function is also introduced in addition to the sine and cosine functions, enabling the method to maintain the direction of the current optimal solution to a certain extent. Finally, the superiority and effectiveness of the method compared with traditional methods are verified through some comparative simulations, indicating that the method has a strong delay prediction ability. © 2023 IEEE.

关键词： Forecasting

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Fusion visual Saliency with Dual-Spectral Saliency of Anomaly Detection for Hyperspectral Herbal Oral Liquid 9

Fusion Visual Saliency with Dual-Spectral Saliency of Anomal...

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9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Yin, Ating Mao, Jianxu Wang, Yaonan Zeng, Kai Zhang, Hui Li, Yaping College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center of Robot Vision Perception and Control Technology Changsha China

ISBN: (纸本)9781510664432

Chinese herbal oral liquid can leach a variety of effective ingredients from herbs and has become a major drug for clinical application. However, it is easy to produce or introduce foreign matters that are very faint in the automatic filling production process. To solve the challenge of low accuracy of faint foreign matter detection, in this paper, we proposed a salient-based anomaly detection method which is fuses visual saliency with dual-spectral saliency (VDS) for the hyperspectral herbal oral liquid. Specifically, we first select the most discriminative bands via the band selection method to generate the pseudo-color map. Subsequently, the histogram-based contrast method is introduced to select the saliency feature map with the largest variance of color features, while fusing the multi-scale gradient features to obtain the preliminary vision-based anomaly detection map. After that, the spectral angles and spectral Euclidean distances are calculated separately based on the oral liquid hyperspectral images to fused into dual-spectral saliency maps. Finally, the dual-spectral saliency map is employed to suppress the background information of the preliminary anomaly detection map. The experimental results show that our proposed method outperforms the state-of-the-art anomaly detection methods, which accurately and quickly achieve the detection of faint foreign matter in the hyperspectral herbal oral liquid. It will accelerate the process of automated filling production lines for oral liquid in the pharmaceutical industry. © 2023 SPIE.

关键词： Anomaly detection

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Posture-based Leader-follower Formation for Multiple Mobile robot Systems Using Data-based Adaptive Iterative Learning control

Posture-based Leader-follower Formation for Multiple Mobile ...

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2022 Chinese Automation Congress, CAC 2022

作者： Chaochen, Zhuolei Wang, Yaonan Tan, Haoran Zhou, Jiaming Lin, Qiong National Engineering Research Center for Robot Visual Perception and Control College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (纸本)9781665465335

Multiple mobile robot systems (MMRSs) have great potential in various fields and their reliable application depends on accurate formation among robots. Focusing on the formation problem of MMRSs, a posture-based adaptive iterative learning control scheme (PB-AILC) is proposed in this paper. Firstly, the leader-follower formation model is built. Then PB-AILC scheme is introduced to optimize the control linear and angular velocity of follower robots at each iteration based on the robots' global posture only, guaranteeing that followers can steadily track their leader with prescribed relative distance and orientation. Meanwhile, we analyze its feasibility mathematically. Finally, numerical simulations illustrate the validity of the PB-AILC scheme and we compare PB-AILC with an iterative learning control (ILC) method to demonstrate its superiority. © 2022 IEEE.

关键词： Iterative methods

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