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arXiv 2022年

作者： Zhang, Zhuqing Jiao, Yanmei Huang, Shoudong Wang, Yue Xiong, Rong State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou China Centre for Autonomous Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Drift-free localization is essential for autonomous vehicles. In this paper, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odometry and the measurements of the features in the pre-built map. In this framework, the transformation between the odometry frame and the map frame is augmented into the state and estimated on the fly. Besides, we maintain only the keyframe poses in the map and employ Schmidt extended Kalman filter to update the state partially, so that the uncertainty of the map information can be consistently considered with low computational cost. Moreover, we theoretically demonstrate that the ever-changing linearization points of the estimated state can introduce spurious information to the augmented system and make the original four-dimensional unobservable subspace vanish, leading to inconsistent estimation in practice. To relieve this problem, we employ first-estimate Jacobian (FEJ) to maintain the correct observability properties of the augmented system. Furthermore, we introduce an observability-constrained updating method to compensate for the significant accumulated error after the long-term absence (can be 3 minutes and 1 km) of map-based measurements. Through simulations, the consistent estimation of our proposed algorithm is validated. Through real-world experiments, we demonstrate that our proposed algorithm runs successfully on four kinds of datasets with the lower computational cost (20% time-saving) and the better estimation accuracy (45% trajectory error reduction) compared with the baseline algorithm VINS-Fusion, whereas VINS-Fusion fails to give bounded localization performance on three of four datasets because of its inconsistent estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

arXiv

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arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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A Small UAV Detection Method Based on Optical Flow and Visual Feature Fusion

A Small UAV Detection Method Based on Optical Flow and Visua...

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International Conference on Communication Technology (ICCT)

作者： Miao Li Hanzhuo Wang Shengjian Mao Zhiguo Shi Ran Tao College of Control Science and Engineering Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province School of Information and Electronics Beijing Institute of Technology Beijing China

The rapid growth of the unmanned aerial vehicle (UAV) market poses threats to public safety and personal privacy. Visual detection has become a cost-effective and important method for UAV detection, but it struggles with detecting small and distant UAVs that lack clear morphological features. To address this, we propose Fusion Net, a network detection approach that combines optical flow features with visual features for improved detection. Fusion Net utilizes Convolutional Neural networks (CNNs) and Transformers for feature extraction and fusion, achieving excellent detection results. We also introduce Mask Augmentation, a new data augmentation method, to enhance network convergence and diversity in UAV scenes. Fusion Net, pre-trained on the Drone-vs-Bird dataset, exhibits excellent performance in detecting small drones, as demonstrated by its high detection accuracy in our self-made dataset evaluation.

关键词：

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Ray-Tracing based CDL Modeling for C-V2X Scenarios with Multiple Roadside Vehicles

Ray-Tracing based CDL Modeling for C-V2X Scenarios with Mult...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yiran Wang Danping He Hao Duo Hao An Ke Guan Dao Tian Andrej Hrovat School of Electronic and Information Engineering Beijing Jiaotong University Beijing China CTTL-Terminal China Academy of Information and Communication Technology Beijing China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen China Department of Communication Systems Jožef Stefan Institute Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

Cellular vehicle-to-everything (C-V2X) communication has provided safer and more convenient interactions between vehicles and surroundings. To support the improvement of intelligent C-V2X communication, it is essential to understand the characteristics of the wireless channel. In this paper, channel measurement is performed in the urban C-V2X scenario at 5.915 GHz. The material electromagnetic (EM) parameters are calibrated based on the measured results, which are used for multipath data obtaining in ray-tracing (RT) simulation. After that, the clustered delay line (CDL) models for the C-V2X scenario are proposed to study the spatial characteristics of the significant multipath components. Related cluster and in-cluster parameters are extracted and analyzed based on CDL modeling. This paper provides useful calibrated EM parameters for extensive simulations. The complex channel data in the C-V2X scenarios with and without multiple roadside vehicles are effectively simulated and studied. The proposed CDL models can offer guidance for channel modeling research in the C-V2X scenario.

关键词： Wireless communication Base stations Simulation Ray tracing Delay lines Information and communication technology Delays

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Distributed Optimization with Projection-free Dynamics

arXiv

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arXiv 2021年

作者： Chen, Guanpu Yi, Peng Hong, Yiguang Chen, Jie The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Department of Control Science and Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China

We consider continuous-time dynamics for distributed optimization with set constraints in the paper. To handle the computational complexity of projection-based dynamics due to solving a general quadratic optimization subproblem with projection, we propose a distributed projection-free dynamics by employing the Frank-Wolfe method, also known as the conditional gradient algorithm. The process searches a feasible descent direction by solving an alternative linear optimization instead of a quadratic one. To make the approach applicable over weight-balanced digraphs, we design a dynamics for the consensus of local decision variables and another dynamics of auxiliary variables to track the global gradient. Then we prove the convergence of the dynamical systems to the optimal solution, and provide detailed numerical comparisons with both projection-based dynamics and other distributed projection-free algorithms. Also, we derive the distributed discrete-time scheme following the instructive ideas of the proposed dynamics and provide its accordingly convergence rate. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamics

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Bayesian Framework for Multi-User Cooperative Radio SLAM

Bayesian Framework for Multi-User Cooperative Radio SLAM

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hang Que Jie Yang Tao Du Shuqiang Xia Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China ZTE Corporation Shenzhen China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by $\mathbf{4 8. 5 \%}$ and UE localization accuracy by $13.5 \%$, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings.

关键词： Location awareness Simultaneous localization and mapping Accuracy Communication systems Heuristic algorithms Simulation Bayes methods Sensors Millimeter wave communication Land mobile radio

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NeurAR: Neural Uncertainty for autonomous 3D Reconstruction with Implicit Neural Representations

arXiv

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arXiv 2022年

作者： Ran, Yunlong Zeng, Jing He, Shibo Chen, Jiming Li, Lincheng Chen, Yingfeng Lee, Gimhee Ye, Qi The College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Fuxi AI Lab NetEase Inc. Hangzhou China The Department of Computer Science National University of Singapore Singapore

Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality;secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://***/NeurAR/. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

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Federated-TD3:Joint Computing Offloading and Resource Allocation in Urban Air Mobility

Federated-TD3:Joint Computing Offloading and Resource Alloca...

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhonghao Luo Chunyu Pan Sihua Wang Linyan Guo The Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China The Department of Electronics and Communication Engineering Hong Kong University of Science and Technology Hong Kong China School of Geophysics and Information Technology China University of Geosciences Beijing Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

With rapid urban population growth and urban-ization, along with increasing demand for developing the low- altitude economy, urban air mobility (UAM) is seen as a way to alleviate ground traffic congestion and promote the low-altitude economy. Due to frequent data computation, low latency require-ments, and limited energy, effective computation offloading and resource management are urgently needed. This paper assumes UAM aircraft transmit semantic information and proposes a computation offloading and resource allocation strategy based on edge computing (EC). In case of edge server failure, relay UAV s assist in information transmission, enabling UAM aircraft to offload tasks to other edge servers. We formulated an op-timization problem to minimize delay and energy consumption and designed the Federated- Td3algorithm, combining federated learning (FL) and twin delayed deep deterministic policy gradient (TD3). Compared to two baseline algorithms, our algorithm reduced delay by 9.9% and 3.3%, and energy consumption by 14.1 % and 4.5%.

关键词： Energy consumption Semantics Delays Resource management Servers Aircraft Relays Traffic congestion Edge computing Testing

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Learned Intelligent Recognizer with Adaptively Customized RIS Phases in Communication systems

Learned Intelligent Recognizer with Adaptively Customized RI...

引用

Future Communications and networks (FCN), International Conference on

作者： Yixuan Huang Jie Yang Chao-Kai Wen Shuqiang Xia Xiao Li Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan ZTE Corporation Shenzhen China State Key Laboratory of Mobile Network and Mobile Multimedia Shenzhen China

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

This study presents an advanced wireless system that embeds target recognition within reconfigurable intelligent surface (RIS)-aided communication systems, powered by cuttingedge deep learning innovations. Such a system faces the challenge of fine-tuning both the RIS phase shifts and neural network (NN) parameters, since they intricately interdepend on each other to accomplish the recognition task. To address these challenges, we propose an intelligent recognizer that strategically harnesses every piece of prior action responses, thereby ingeniously multiplexing downlink signals to facilitate environment sensing. Specifically, we design a novel NN based on the long short-term memory (LSTM) architecture and the physical channel model. The NN iteratively captures and fuses information from previous measurements and adaptively customizes RIS configurations to acquire the most relevant information for the recognition task in subsequent moments. Tailored dynamically, these configurations adapt to the scene, task, and target specifics. Simulation results reveal that our proposed method significantly outperforms the state-of-the-art method, while resulting in minimal impacts on communication performance, even as sensing is performed simultaneously.

关键词： Wireless communication Multiplexing Technological innovation Phase measurement Target recognition Simulation Memory architecture Reconfigurable intelligent surfaces Sensors Long short term memory

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