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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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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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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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Adaptive Fixed-time Fuzzy Tracking control of Uncertain robot 27

Adaptive Fixed-time Fuzzy Tracking Control of Uncertain Robo...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Zhou, Beibei Zhu, Chengzhi Jiang, Yiming Guangzhou Jianxin Technology Co. Ltd Guangzhou510640 China South China University of Technology Key Laboratory of Autonomous Systems and Net-worked Control College of Automation Science and Engineering Guangzhou510640 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Hunan Changsha410082 China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

We propose a novel fixed-time fuzzy control strategy to achieve the trajectory tracking of the robot systems with uncertainties in this article. The tracking errors of the system can be convergent to a small area around zero with fixed-time convergence rate, combining with the fuzzy logic system and the backstepping technique. Besides, the boundedness of all the closed-loop system signals can be proved under the proposed controller. The superiority performance of the proposed control strategy has been shown by the simulation results. © 2022 IEEE.

关键词： Closed loop systems

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

arXiv

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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

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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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LF-PGVIO: A visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China School of Computing and Engineering University of Huddersfield Huddersfield U.K State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

In this paper, we propose LF-PGVIO, a visual-Inertial-Odometry (VIO) framework for large Field-of-View (FoV) cameras with a negative plane using points and geodesic segments. The purpose of our research is to unleash the potential of point-line odometry with large-FoV omnidirectional cameras, even for cameras with negative-plane FoV. To achieve this, we propose an Omnidirectional Curve Segment Detection (OCSD) method combined with a camera model which is applicable to images with large distortions, such as panoramic annular images, fisheye images, and various panoramic images. The geodesic segment is sliced into multiple straight-line segments based on the radian and descriptors are extracted and recombined. Descriptor matching establishes the constraint relationship between 3D line segments in multiple frames. In our VIO system, line feature residual is also extended to support large-FoV cameras. Extensive evaluations on public datasets demonstrate the superior accuracy and robustness of LF-PGVIO compared to state-of-the-art methods. The source code will be made publicly available at https://***/flysoaryun/LF-PGVIO. IEEE

关键词： Feature extraction

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Forecasting of the Incoming Dustcarts of a Waste Transfer Station Based on SARIMA Model

Forecasting of the Incoming Dustcarts of a Waste Transfer St...

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第35届中国控制与决策会议

作者： Jiakang Zhou Qiu Fang Han Zhu Haojie Song Yaonan Wang the National Engineering Reserch Center for Robot Visual Perception and Control Technology Hunan University Shanghai Chengtou Environment (Group) Co.

The quantity forecast of incoming dustcarts in the waste transfer station is essential to enhancing the operational efficiency of smart sanitation,because it is helpful for the station management and the planning of *** this study,a seasonal autoregressive integrated moving average(SARIMA)model is suggested to forecast the incoming dustcarts of a waste transfer *** dataset utilized contains both the hourlysampled quantity and proportion of residual waste *** outcomes of single step and multi-step forecasting are examined with different performance measures in order to confirm SARIMA's *** experimental results show that the SARIMA model has better prediction results compared with the LSTM *** addition,SARIMA model has high accuracy in both single step and multi-step forecasting,but multistep forecasting is more effective for solving real-world issues because of its less time-consuming.

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Double-layer Descriptors and High-dimensional Search Mechanisms for Partial Point Cloud Registration

Double-layer Descriptors and High-dimensional Search Mechani...

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Chinese Automation Congress (CAC)

作者： Zhiyu Wang Qiu Fang Yaonan Wang Hong Lu Jun Ye National Engineering Research Center for Robot Visual Perception and Control Technology School of Electrical and Information Engineering Hunan University Changsha Hunan China

Partial point cloud registration is an essential preprocessing technique to generate complete 3D shapes that aim to transform partial scans into a common coordinate system. Existing methods that utilize geometric alignment rarely combine local and global information, which results in many incorrect correspondences. In the paper, a two-layer descriptor and a high-dimension searching approach (DDHSM) are utilized to extract and select the correct correspondences. Firstly, in the first layer, the Fast Point Feature Histogram (FPFH) is used to describe the feature of every point with high-dimension information based on its robustness and fastness. Then a high-dimensional search is considered to extract the initial corresponding set. In the second layer, global information is introduced to find the correct correspondences. For each point pair in the initial corresponding set, the normal angles are compared for all remaining points that satisfy the distance requirement. Finally, the singular value decomposition (SVD) method is implemented to compute the rigid transformation based on the updated corresponding set. Experimental results and comparisons with state-of-the-art methods demonstrate the effectiveness and feasibility of our approach.

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