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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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Self-Contrastive Learning Fault Diagnosis Network for Signal Imaging: A Case Study

Self-Contrastive Learning Fault Diagnosis Network for Signal...

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Chinese Automation Congress (CAC)

作者： Wenbin He Jianxu Mao Yaonan Wang Zhe Li Feng Yan Kexin Liu College of Electrical & Information Engineering and National Engineering Research Center of Robot Visual Perception & Control Technology Hunan University Hunan China College of Control Science & Engineering and State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The deep convolution method based on MSDP signal imaging has been proven to be an effective means of monitoring the robot grinding process. This method has very high requirements on the quality of imaging and requires a lot of computing resources. This paper proposes an image self-contrastive learning network that can effectively extract fault information from low-pixel fuzzy images and achieve efficient and accurate condition monitoring. The network first calculates the correlation distance matrix between two identical modified symmetrized dot pattern (MSDP) images and uses the generated similarity labels to calculate the contrast loss to extract the embedded features of the image. Finally, the classification network is trained using the cross entropy loss to achieve the classification of fault images. The method was verified on a robotic grinding platform to significantly reduce the calculation time while maintaining the recognition accuracy, which provides reliable technical support for the real-time monitoring of the robotic grinding process.

关键词： Fault diagnosis Vibrations Accuracy Image recognition Imaging Feature extraction Real-time systems robots Monitoring Image classification

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A Muti-stage Selection Filter Based on Wavelet Packet and 2DCNN for Fault Diagnosis of Rotating Machinery 42

A Muti-stage Selection Filter Based on Wavelet Packet and 2D...

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42nd Chinese control Conference, CCC 2023

作者： He, Wenbin Mao, Jianxu Liu, Li Li, Zhe Yang, Miao Wang, Yaonan College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China State Grid Hunan Electric Power Company Limited Research Institute Changsha410007 China

ISBN: (纸本)9789887581543

Fault information of rotating machinery is often drowned in strong noise signals, so it is crucial to accurately identify faults from high-intensity noise signals. In this article, an end-to-end fault diagnosis model is developed, which consists of a multi-stage selection filter based on wavelet packet and 2D-CNN. First, the original measured mechanical signals were processed by the three-level wavelet packet decomposition to obtain eight sub-bands with coefficient matrices. Second, the signal is reconstructed using different numbers of sub-bands, where the number is increased by one at a time to obtain eight different multi-stage reconstructed signals. Third, the reconstructed signals are reorganized into 2D signal maps;and a parallel training network is constructed using signal maps and 2D-CNN to achieve fault classification. Then, guided by the training results, eight parallel classification results are compared, so as to train the best fault diagnosis model. Finally, the simulation experiment based on a bearing data set illustrates the proposed multi-stage selection filter is effective and feasible in application. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Failure analysis

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An intelligent route recommendation method for smart Internet-connected electric vehicles under the coupling of traffic and electricity

An intelligent route recommendation method for smart Interne...

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第42届中国控制会议

作者： JinhuanXie Dongqi Liu ZhenwuYang School of Electrical and Information Engineering Changsha University of Science and Technology National Engineering Research Center for Robot Visual Perception and Control Technology

Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario,this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of *** model integrally balances the interests of users and power grid in the coupled road-electric network,and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback,considering the influence of vehicle departure time and remaining power in short time *** is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model.

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Motion Planning for Mobile robots with Temporal Logic Specifications

Motion Planning for Mobile Robots with Temporal Logic Specif...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Jianxin Zeng Yaonan Wang Zhiqiang Miao Sifei Wang College of Electrical and Information Engineering and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China

In this paper, we propose a motion planning method for mobile robots in order to satisfy task requirements specified in linear temporal logic (LTL). The proposed method follows the traditional hierarchical planning workflow, including path planning and trajectory planning. Firstly, we propose a path planning method based on rapidly exploring random trees (RRT*) and Dubins curve. The tree derived from RRT* is expanded with the help of Buchi automaton converted by an LTL formula. The proposed method can automatically adjust the iterations of sampling according to the scale of Buchi automata. Then, we formulate an optimization problem for the generation of trajectory. We use Bezier curve to express the trajectory and use differential flatness of mobile robots to simplify the optimization problem. Finally, we validate the practicality of our method through simulations and real-world experiments.

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EgoEvGesture: Gesture Recognition Based on Egocentric Event Camera

arXiv

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arXiv 2025年

作者： Wang, Luming Shi, Hao Yin, Xiaoting Yang, Kailun Wang, Kaiwei Bai, Jian State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

Egocentric gesture recognition is a pivotal technology for enhancing natural human-computer interaction, yet traditional RGB-based solutions suffer from motion blur and illumination variations in dynamic scenarios. While event cameras show distinct advantages in handling high dynamic range with ultra-low power consumption, existing RGB-based architectures face inherent limitations in processing asynchronous event streams due to their synchronous frame-based nature. Moreover, from an egocentric perspective, event cameras record data that includes events generated by both head movements and hand gestures, thereby increasing the complexity of gesture recognition. To address this, we propose a novel network architecture specifically designed for event data processing, incorporating (1) a lightweight CNN with asymmetric depthwise convolutions to reduce parameters while preserving spatiotemporal features, (2) a plug-and-play state-space model as context block that decouples head movement noise from gesture dynamics, and (3) a parameter-free Bins-Temporal Shift Module (BSTM) that shifts features along bins and temporal dimensions to fuse sparse events efficiently. We further establish the EgoEvGesture dataset, the first large-scale dataset for egocentric gesture recognition using event cameras. Experimental results demonstrate that our method achieves 62.7% accuracy tested on unseen subjects with only 7M parameters, 3.1% higher than state-of-the-art approaches. Notable misclassifications in freestyle motions stem from high interpersonal variability and unseen test patterns differing from training data. Moreover, our approach achieved a remarkable accuracy of 97.0% on the DVS128 Gesture, demonstrating the effectiveness and generalization capability of our method on public datasets. The dataset and models are made available at https://***/3190105222/EgoEv_Gesture. Copyright © 2025, The Authors. All rights reserved.

关键词： Gesture recognition

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An Efficient Generative Intelligent Multiobjective Grasping Model for Kitchen Waste Sorting

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Deng, Songyun Pei, Ruiyuan Zhou, Li Qin, Hai Sun, Wei Liang, Qiaokang Hunan University College of Electrical and Information Engineering Changsha410082 China Hunan University National Engineering Laboratory for Robot Vision Perception and Control College of Electrical and Information Engineering Changsha410082 China

The key challenge in automated kitchen waste sorting on conveyor belts is how robots execute grasping tasks. Current methods, dependent on object detection, struggle to adopt generative architectures due to data scarcity and algorithmic inefficiency. To solve these issues, this article proposes solutions in three aspects: dataset construction, annotation data processing, and generative model lightweighting. First, we built the KWG2024 dataset, which includes 3116 RGB-D images, 18231 standard annotations, and three new evaluation metrics. Second, we proposed two novel annotation processing methods to address the issue of excessive and incorrect grasps generated by generative networks in single-attempt grasp planning of multiobjects. Third, we constructed an efficient network with classification capabilities, AMI-LiteNet, by integrating the advantages of PP-LiteSeg and AMI-NanoNet. On the public Cornell dataset, AMI-LiteNet achieved a grasping accuracy of 98.3% and an efficiency of 118 frames per second (FPS). In extensive comparative experiments on KWG2024, our model reached a leading computational efficiency compared with recent state-of-the-art methods. It acquired a grasp planning generation accuracy of 87.96% while only requiring a computational effort of 2.79 GFLOPs. Finally, our model was deployed on an edge computing device and achieved a maximum grasping accuracy of 90.91% and a classification accuracy (CA) of 97.48% in our actual robotic system. To our knowledge, this is the first time that a generative grasp method has been applied independently to kitchen waste sorting tasks on a conveyor belt. © 1963-2012 IEEE.

关键词： Belt conveyors

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Learning Granularity-Aware Affordances from Human-Object Interaction for Tool-Based Functional Grasping in Dexterous robotics

arXiv

引用

arXiv 2024年

作者： Yang, Fan Chen, Wenrui Yang, Kailun Lin, Haoran Luo, DongSheng Tang, Conghui Li, Zhiyong Wang, Yaonan The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features of objects serve as a bridge in the functional interaction between agents and objects. However, leveraging these affordance cues to help robots achieve functional tool grasping remains unresolved. To address this, we propose a granularity-aware affordance feature extraction method for locating functional affordance areas and predicting dexterous coarse gestures. We study the intrinsic mechanisms of human tool use. On one hand, we use fine-grained affordance features of object-functional finger contact areas to locate functional affordance regions. On the other hand, we use highly activated coarse-grained affordance features in hand-object interaction regions to predict grasp gestures. Additionally, we introduce a model-based post-processing module that includes functional finger coordinate localization, finger-to-end coordinate transformation, and force feedback-based coarse-to-fine grasping. This forms a complete dexterous robotic functional grasping framework GAAF-Dex, which learns Granularity-Aware Affordances from human-object interaction for tool-based Functional grasping in Dexterous robotics. Unlike fully-supervised methods that require extensive data annotation, we employ a weakly supervised approach to extract relevant cues from exocentric (Exo) images of hand-object interactions to supervise feature extraction in egocentric (Ego) images. Correspondingly, we have constructed a small-scale dataset, FAH, which includes near 6K images of functional hand-object interaction Exo images and Ego images of 18 commonly used tools performing 6 tasks. Extensive experiments on the dataset demonstrate that our method outperforms state-of-the-art methods, and real-world localization and grasping experiments validate the practical applicability of our approach. The source code and the establishe

关键词： Extraction

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A Graph Reconstruction by Dynamic Signal Coefficient for Fault Classification

arXiv

引用

arXiv 2023年

作者： He, Wenbin Mao, Jianxu Wang, Yaonan Li, Zhe Fang, Qiu Wu, Haotian The College of Electrical and Information Engineering The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Specifically, the original mechanical signal is first decomposed by wavelet packet decomposition (WPD) to obtain multiple sub-bands including coefficient matrix. Then, with originally defined two feature extraction factors MDD and DDD, a dynamic feature selection method based on L2 energy norm (DFSL) is proposed, which can dynamically select the feature coefficient matrix of WPD based on the difference in the distribution of norm energy, enabling each sub-signal to take adaptive signal reconstruction. Next the coefficient matrices of the optimal feature sub-bands are reconstructed and reorganized to obtain the feature signal graphs. Finally, deep features are extracted from the feature signal graphs by 2D-Convolutional neural network (2D-CNN). Experimental results on a public data platform of a bearing and our laboratory platform of robot grinding show that this method is better than the existing methods under different noise intensities. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Velocity-free Formation control for Quadrotors Using High-order Sliding Mode Differentiator

Velocity-free Formation Control for Quadrotors Using High-or...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Qiong Lin Zhiqiang Miao Yaonan Wang College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China

This paper deals with the problem of achieving formation control for underactuated multiple quadrotors without velocity measurements by employing the higher-order sliding mode (HOSM) differentiator. The primary objective of this work is to attain a coordinated formation behavior among the quadrotor group. In this paper, the dynamics of each quadrotor are transformed into a new form, wherein the attitude is described using rotation matrices. Subsequently, an intermediate guide is designed using the HOSM differentiator, taking into account the desired relative positions between the quadrotors and their actual positions. Next, the actual control thrusts and torques for each quadrotor are designed and solved by the intermediate guide. Finally, rigorous theoretical analysis and multiple simulations are performed to ensure that the multiple quadrotors converge asymptotically and accurately to the desired formation shape.

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