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IEEE International Conference on robotics and Biomimetics

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

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.

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Bridging on-line systems modeling with fault detection for a class of unknown nonlinear distributed parameter systems

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IFAC-PapersOnLine 2022年第6期55卷 228-233页

作者： Yun Feng Yaonan Wang Yazhi Zhang College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Changsha 410082 China College of Electric and Information Engineering Hunan Institute of Engineering Xiangtan 411104 China

Different from the traditional model-based fault diagnosis paradigm which is established upon the well-known observer design and analysis, a novel data-driven framework is proposed by combing systems modeling with fault detection for a class of 1-D unknown distributed parameter systems. The key idea is to transfer the on-line modeling error into the residual signal for fault detection. The proposed methodology only utilizes the I/O data and does not require extra knowledge of the system model, which increases its usability at large. Numerical simulations on a commonly used benchmark are presented for method validation.

关键词： AI FDI methods Neural approximations for optimal control estimation FDI for nonlinear Systems Distributed parameter systems

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A Muti-stage Selection Filter Based on Wavelet Packet and2DCNN for Fault Diagnosis of Rotating Machinery

A Muti-stage Selection Filter Based on Wavelet Packet and2DC...

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第42届中国控制会议

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

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.

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Beyond the Field-of-View: Enhancing Scene Visibility and perception with Clip-Recurrent Transformer

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Shi, Hao Jiang, Qi Yang, Kailun Yin, Xiaoting Ni, Huajian Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation and the National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou China School of Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China Shanghai SUPREMIND Technology Company Ltd Shanghai China

Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide sufficient coverage. Nevertheless, from a spatiotemporal perspective, it is possible to obtain information beyond the camera's physical FoV from past video streams. In this paper, we propose the concept of online video inpainting for autonomous vehicles to expand the field of view, thereby enhancing scene visibility, perception, and system safety. To achieve this, we introduce the FlowLens architecture, which explicitly employs optical flow and implicitly incorporates a novel clip-recurrent transformer for feature propagation. FlowLens offers two key features: 1) FlowLens includes a newly designed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated over time. 2) It integrates a multi-branch Mix Fusion Feed Forward Network (MixF3N) to enhance the precise spatial flow of local features. To facilitate training and evaluation, we derive the KITTI360 dataset with various FoV mask, which covers both outer- and inner FoV expansion scenarios. We also conduct quantitative assessments of beyond-FoV semantics across different models and perform qualitative comparisons of beyond-FoV object detection. We illustrate that employing FlowLens to reconstruct unseen scenes even enhances perception within the field of view by providing reliable semantic context. Extensive experiments and user studies involving offline and online video inpainting, as well as beyond-FoV perception tasks, demonstrate that FlowLens achieves state-of-the-art performance. The source code and dataset are made publicly available at https://***/MasterHow/FlowLens. IEEE

关键词： Cameras

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Formation control with Connectivity Maintenance for Double-Integrator Multi-Agent Systems

Formation Control with Connectivity Maintenance for Double-I...

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第三十八届中国控制会议

作者： Zhiqiang Miao Yaonan Wang College of Electrical and Information Engineering Hunan University Shenzhen Research Institute of Hunan University National Engineering Laboratory for Robot Visual Perception and Control

The network connectivity maintenance problem in formation control of mobile agents with double-integrator dynamics is addressed in this paper. Distributed potential field-based controllers are proposed for double-integrator agent group to maintain the connectivity of communication graph until the desired formation is achieved. We also investigate the connectivity preserving formation control with a virtual leader. Providing that at least one agent has information about the leader, all agents can asymptotically reach the desired formation and attain the same velocity as the virtual leader. The asymptotic stability analysis is presented with Lyapunov-like tools including LaSalle's invariance principle and Barbalat's lemma. Simulation results on a group of agents with double-integrator dynamics are presented to demonstrate the effectiveness of the proposed strategies.

关键词： formation control multi-agent systems connectivity maintenance

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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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IEEE International Conference on Systems, Man and Cybernetics

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

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.

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Multi-Agent Path Finding Using Imitation-Reinforcement Learning with Transformer

Multi-Agent Path Finding Using Imitation-Reinforcement Learn...

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IEEE International Conference on robotics and Biomimetics

作者： Lin Chen Yaonan Wang Zhiqiang Miao Yang Mo Mingtao Feng Zhen Zhou School of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Technology Changsha China School of Computer Science and Technology Xidian University Xian China

ISBN: (纸本)9781665481106

Multi-Agent Path Finding is a problem of finding the optimal set of paths for multiple agents from the starting position to the goal without conflict, which is essential to large-scale robotic systems. Imitation and reinforcement learning are applied to solve the MAPF problem and have achieved certain results, which provides a feasible solution for the path planning problem of large-scale robot systems. The current method improves the performance of distributed strategy-guided agent planning paths in complex environments by introducing the communication between graph neural networks and agents but dramatically reduces the system's robustness. This paper develops a novel imitation reinforcement learning framework by introducing Transformer, which enables algorithms to perform well in complex environments without relying on communication between agents. Compared with its counterparts, experiments show that the policy trained by our method guides the agent to drive from the initial position to the goal without collision and achieve better performance.

关键词： Learning systems robot kinematics Biomimetics Reinforcement learning Transformers Robustness Path planning

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Single-Leader-Dual-Follower Teleoperation in Object-Holding Task with Internal Force Regulation 26

Single-Leader-Dual-Follower Teleoperation in Object-Holding ...

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26th International Conference on Automation and Computing, ICAC 2021

作者： Huang, Darong Jiang, Yiming Yang, Chenguang South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Changsha410082 China University of the West of England Bristol Robotics Laboratory BristolBS16 1QY United Kingdom

ISBN: (纸本)9781860435577

robot teleoperation attracts growing attention of researchers in many domains. Plenty of factors contribute to the good performance of a smart teleoperation system, and one crucial factor is that it provides an environmental feedback for the operator. Hence, in this paper, we offer a solution to help operator to perceive the contact force of the tele-robot. In addition, we introduce a strategy to cooperatively teleoperate two follower robots by using only single leader1 robot, which helps to reduce the control burden for the operator, as well as enables the teleoperation system to avoid the problem caused by the mismatched degree of freedom (DoF). Moreover, an impedance model based technique is employed to regulate the holding internal force during object-holding tasks. Experiments are conducted gradually, using a Touch X device as the leader robot and two arms of the Baxter robot as the tele-robots. Experimental results validate the feasibility of the proposed teleoperation system. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Remote control

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An insulator feature-enhanced segmentation method based on the residual-type attention mechanism

An insulator feature-enhanced segmentation method based on t...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Xin Tang Jianxu Mao Junfei Yi Ziming Tao Zhenyu He College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Changsha China

ISBN: (纸本)9781665479691

As an crucial component of power line, the timely check for the state of insulator is necessary for the normal running of transmission lines. In view of debasement of insulator segmentation accuracy in current transmission line caused by complex background, low contrast, and the quality of images is not guaranteed, we improve U-Net by combining attention mechanism and residual connection. Residual connection is added to the encoder part to improve the extraction of low-level semantic information, and the attention mechanism is added to the decoder part for integrating high and low level features better and reduce the error between them. We confirm the effectiveness of the improved module through experiments, while the results showing that the improved U-Net model segmentation performance on insulator dataset is improved from 0.875 to 0.912. And our method also outperforms previous segmentation work on insulator dataset.

关键词： Image segmentation Power transmission lines Automation Semantics Insulators Feature extraction Information management

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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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Chinese Automation Congress (CAC)

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

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.

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