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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Jianxin Zeng Yaonan Wang Zhiqiang Miao Wei He Hesheng Wang Department of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Intelligence Science and Technology University of Science and Technology Beiing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

For aerial swarms, formation flight has been applied in various scenes. However, most existing works do not consider balancing the conflicting requirements among keeping formation, keeping the smoothness of trajectories, and obstacle avoidance within the limited time. To address this issue, we propose a decentralized trajectory planning framework for formation flight in unknown and dense environments. To ensure that feasible trajectories can be found within the limited time, the formation optimization problem is decoupled into formation affine transformation and iterative trajectory generation. Firstly, the optimization problem based on affine transformation is designed to obtain the optimal affine transformation sequence, which provides the formation reference of trajectory optimization. Secondly, the iterative optimization framework of trajectory planning is designed, which balances the conflicting requirements of formation, smooth flight, and obstacle avoidance. Besides, to escape the local minima caused by non-convex dense environments, the method of topological path planning is designed to provide distinctive initial solutions for trajectory optimization. Finally, the proposed methods are proven to be effective through the simulations and real-world experiments.

关键词： Trajectory planning Planning Iterative methods Collision avoidance Trajectory optimization Intelligent robots

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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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Robust image-based landing control of a quadrotor on an unknown moving platform using circle features

Robust image-based landing control of a quadrotor on an unkn...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Lin, Jie Wang, Yaonan Miao, Zhiqiang Zhong, Hang Nie, Jingmou Fierro, Rafael College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Visual Perception and Control Changsha China University of New Mexico MARHES Lab Department of Electrical and Computer Engineering AlbuquerqueNM87131-0001 United States

ISBN: (纸本)9781665436786

This paper addresses the robust image-based landing control problem for a landing system, including an underactuated quadrotor and an unknown moving platform. Firstly, an image kinematics is constructed by using an image moment based on a circle feature in the virtual image plane, which decouples the landing system into a translation and a rotation control module. Exploiting the funnel control techniques with barrier Lyapunov functions (BLFs), the proposed IBVS control law does not depend on relative height needed by IBVS-like approaches and the explicit knowledge of moving platforms. Besides, it guarantees feature errors with prescribed transient and steady-state behavior. Finally, A simulation study is presented to verify the efficacy of the proposed control scheme. © 2021 IEEE.

关键词： Lyapunov functions

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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

arXiv

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arXiv 2023年

作者： Wang, Ze Yang, Kailun Shi, Hao Zhang, Yufan Xu, Zhijie Gao, Fei Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 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. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds

arXiv

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arXiv 2023年

作者： He, Yong Yu, Hongshan Yang, Zhengeng Sun, Wei Feng, Mingtao Mian, Ajmal The National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Lushan South Rd. Yuelu Dist. Changsha410082 China The School of Computer Science and Technology Xidian University Xi’an710071 China The Department of Computer Science The University of Western Australia PerthWA6009 Australia

Local features and contextual dependencies are crucial for 3D point cloud analysis. Many works have been devoted to designing better local convolutional kernels that exploit the contextual dependencies. However, current point convolutions lack robustness to varying point cloud density. Moreover, contextual modeling is dominated by non-local or self-attention models which are computationally expensive. To solve these problems, we propose density adaptive convolution, coined DAConv. The key idea is to adaptively learn the convolutional weights from geometric connections obtained from the point density and position. To extract precise context dependencies with fewer computations, we propose an interactive attention module (IAM) that embeds spatial information into channel attention along different spatial directions. DAConv and IAM are integrated in a hierarchical network architecture to achieve local density and contextual direction-aware learning for point cloud analysis. Experiments show that DAConv is significantly more robust to point density compared to existing methods and extensive comparisons on challenging 3D point cloud datasets show that our network achieves state-of-the-art classification results of 93.6% on ModelNet40, competitive semantic segmentation results of 68.71% mIoU on S3DIS and part segmentation results of 86.7% mIoU on ShapeNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Modality Unified Attack for Omni-Modality Person Re-Identification

arXiv

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arXiv 2025年

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan the College of Electrical and Information Engineering Hu nan University National Engineering Research Center of Robot Vi sual Perception and Control Technology Hunan Changsha China the College of Information Science and Engineering Hunan Normal University Hunan Provincial Key Laboratory of Intel ligent Computing and Language Information Processing Hunan Changsha China

Deep learning based person re-identification (reid) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality reid models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omni-modality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Memory-augment convolutional Autoencoder for unsupervised pavement crack classification

Memory-augment convolutional Autoencoder for unsupervised pa...

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2021 China Automation Congress, CAC 2021

作者： Wu, Tianyue Zhang, Hui Liu, Jiaxuan Chen, Yurong Yi, Junfei Zhang, Zouquan School of Electrical and Information Engineering Changsha University of Science and Technology Changsha China School of Robotics also with Visual Perception and Control Technology National Engineering Laboratory Hunan University Changsha China School of Electrical and Information Engineering Hunan University Changsha China

ISBN: (纸本)9781665426473

In urban construction, transportation system is an important part. However, the pavement cracks will occur because of using in long time and some external force collision, which has impacts on the safety and reliability of the whole transportation system. Therefore, it is necessary to detect the pavement cracks. The formation of pavement cracks is slow, and it is not concentrated on one place, so it is difficult to obtain enough images containing cracks to train. We propose a new pre-training method based on self-supervision study, which can obtain features closer to the target task from normal pavement images. In addition, we combine a memory-augment convolutional autoencoder and a hard-threshold module to improve the accuracy of the crack classification. Experiments conducted on the Mendeley Concrete crack dataset and DeepCrack dataset demonstrate the good performance in pavement crack classification task. © 2021 IEEE

关键词： Learning systems

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FocusFlow: Boosting Key-Points Optical Flow Estimation for Autonomous Driving

arXiv

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arXiv 2023年

作者： Yi, Zhonghua Shi, Hao Yang, Kailun Jiang, Qi Ye, Yaozu Wang, Ze Ni, Huajian Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd. Shanghai201210 China

Key-point-based scene understanding is fundamental for autonomous driving applications. At the same time, optical flow plays an important role in many vision tasks. However, due to the implicit bias of equal attention on all points, classic data-driven optical flow estimation methods yield less satisfactory performance on key points, limiting their implementations in key-point-critical safety-relevant scenarios. To address these issues, we introduce a points-based modeling method that requires the model to learn key-point-related priors explicitly. Based on the modeling method, we present FocusFlow, a framework consisting of 1) a mix loss function combined with a classic photometric loss function and our proposed Conditional Point control Loss (CPCL) function for diverse point-wise supervision;2) a conditioned controlling model which substitutes the conventional feature encoder by our proposed Condition control Encoder (CCE). CCE incorporates a Frame Feature Encoder (FFE) that extracts features from frames, a Condition Feature Encoder (CFE) that learns to control the feature extraction behavior of FFE from input masks containing information of key points, and fusion modules that transfer the controlling information between FFE and CFE. Our FocusFlow framework shows outstanding performance with up to +44.5% precision improvement on various key points such as ORB, SIFT, and even learning-based SiLK, along with exceptional scalability for most existing data-driven optical flow methods like PWC-Net, RAFT, and FlowFormer. Notably, FocusFlow yields competitive or superior performances rivaling the original models on the whole frame. The source code will be available at https://***/ZhonghuaYi/FocusFlow_official. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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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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