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IEEE International Conference on robotics and Automation (ICRA)

作者： Yu Han Ziwei Long Yanting Zhang Jin Wu Zhijun Fang Rui Fan School of Computer Science and Technology Donghua University Shanghai P. R. China Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and Frontiers Science Center for Intelligent Autonomous Systems Machine Intelligence & Autonomous Systems (MIAS) Group the College of Electronics & Information Engineering Tongji University Shanghai P. R. China Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology P. R. China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM.

关键词： Performance evaluation Accuracy Semantic segmentation Source coding Image matching Supervised learning Pose estimation

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FogROS2-FT: Fault Tolerant Cloud robotics

arXiv

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arXiv 2024年

作者： Chen, Kaiyuan Hari, Kush Chung, Trinity Wang, Michael Tian, Nan Juette, Christian Ichnowski, Jeffrey Ren, Liu Kubiatowicz, John Stoica, Ion Goldberg, Ken Department of Electrical Engineering and Computer Science United States Robert Bosch Research and Technology Center North America SunnyvaleCA United States Robotics Institute Carnegie Mellon University United States Department of Industrial Engineering and Operations Research United States University of California BerkeleyCA United States

— Cloud robotics enables robots to offload complex computational tasks to cloud servers for performance and ease of management. However, cloud compute can be costly, cloud services can suffer occasional downtime, and connectivity between the robot and cloud can be prone to variations in network Quality-of-Service (QoS). We present FogROS2-FT (Fault Tolerant) to mitigate these issues by introducing a multi-cloud extension that automatically replicates independent stateless robotic services, routes requests to these replicas, and directs the first response back. With replication, robots can still benefit from cloud computations even when a cloud service provider is down or there is low QoS. Additionally, many cloud computing providers offer low-cost "spot" computing instances that may shutdown unpredictably. Normally, these low-cost instances would be inappropriate for cloud robotics, but the fault tolerance nature of FogROS2-FT allows them to be used reliably. We demonstrate FogROS2-FT fault tolerance capabilities in 3 cloud-robotics scenarios in simulation (visual object detection, semantic segmentation, motion planning) and 1 physical robot experiment (scan-pick-and-place). Running on the same hardware specification, FogROS2-FT achieves motion planning with up to 2.2x cost reduction and up to a 5.53x reduction on 99 Percentile (P99) long-tail latency. FogROS2-FT reduces the P99 long-tail latency of object detection and semantic segmentation by 2.0x and 2.1x, respectively, under network slowdown and resource contention. Videos and code are available at https://***/view/fogros2-ft. © 2024, CC BY.

关键词： Quality of service

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises

Design and Application of a One-Stop Mobile Application Shar...

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Chinese Control Conference (CCC)

作者： Xinzhe Chen Luefeng Chen Jie Hu Qingfeng Yang Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance.

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Neural-Network-Based Recursive State Estimation for Nonlinear Networked systems With Binary-Encoding Mechanisms

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IEEE Transactions on Neural Networks and Learning systems 2025年第6期36卷 10072-10083页

作者： Yuhan Zhang Zidong Wang Lei Zou Wei Qian Shuxin Du College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University of London Middlesex Uxbridge U.K. College of Information Science and Technology Donghua University Shanghai China Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education Shanghai China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

关键词： Artificial neural networks State estimation Encoding Noise Networked control systems Electronic mail Symmetric matrices Estimation error Tuning Recursive estimation

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High Quality Factor AlScN Lamb Wave Resonators Using NbN/Al Top Electrodes at Cryogenic Temperature 22

High Quality Factor AlScN Lamb Wave Resonators Using NbN/Al ...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Luo, Zhifang Shao, Shuai Dong, Peng Guo, Haowen Zou, Xinbo Li, Jun Lee, Chengkuo Wu, Tao School Of Information Science And Technology ShanghaiTech University China Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences China University Of Chinese Academy Of Sciences China School Of Physical Science And Technology ShanghaiTech University China Shanghai Engineering Research Center Of Energy Efficient And Custom Ai Ic China Department Of Electrical & Computer Engineering National University Of Singapore Singapore Center For Intelligent Sensors And Mems National University Of Singapore Singapore

ISBN: (纸本)9784886864352

Coupling between phonons and qubits provides a solid quantum platform for information transmission and processing. We report the high quality factor (Qa) aluminum scandium nitride (AlScN) Lamb wave resonators (LWRs) at cryogenic temperature for quantum applications. Superconducting 60 nm NbN/30nm Al serves as top electrodes for ultra-low energy dissipation at around 11 K. Al0.84Sc0.16N provides a high piezoelectric constant for high electro-mechanical coupling (kt2) of resonators. Additionally, LWRs allow lithography-defined resonant frequency for flexible multi-frequency phonon sources. The fabricated LWR shows Qa over 6000, and resulting in a record figure-of-merit (FoM = kt2∗Qa) of 211 for AlScN resonators. © 2023 IEEJ.

关键词： AlScN Lamb wave resonator NbN quantum superconducting

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Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

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World Journal of Gastroenterology 2024年第6期30卷 542-555页

作者： Hui-Ting Ge Jian-Wu Chen Li-Li Wang Tian-Xiu Zou Bin Zheng Yuan-Fen Liu Yun-Jing Xue Wei-Wen Lin Department of Radiology Fujian Medical University Union HospitalFuzhou 350001Fujian ProvinceChina Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors Fujian Medical UniversityFuzhou 350001Fujian ProvinceChina Digestive Hematological and Breast MalignanciesClinical Research Center for Radiology and Radiotherapy of Fujian ProvinceFuzhou 350001Fujian ProvinceChina Department of Radiation Oncology Fujian Medical University Union HospitalFuzhou 350001Fujian ProvinceChina Department of Diagnostic Radiology Fujian Medical University Union HospitalFuzhou 350001Fujian ProvinceChina School of Electrical and Computer Engineering University of OklahomaOklahomaOK 73019United States

BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor *** preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment ***,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited *** CT provides quantitative enhancement parameters that may better capture tumor *** hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC *** To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status *** This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor ***,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine co

关键词： Spectral computed tomography Gastric cancer Lymphovascular invasion Perineural invasion

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Dynamic Spectrum Sharing in Cellular Based Urban Air Mobility via Deep Reinforcement Learning

Dynamic Spectrum Sharing in Cellular Based Urban Air Mobilit...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Ruixuan Han Hongxiang Li Eric J. Knoblock Michael R. Gasper Rafael D. Apaza Department of Electrical and Computer Engineering University of Louisville Louisville KY United States Communications and Intelligent Systems Division NASA Glenn Research Center Cleveland OH United States

ISBN: (纸本)9781665435413

With increasing mobility demands in metropolitan areas, the emerging concept of Urban Air Mobility (UAM) opens a new urban air transportation paradigm, where one big challenge is to ensure reliable two-way communications between aerial vehicles and their ground control stations for safe operations. The concept of cellular-based UAM (cUAM) provides a promising solution where aerial vehicles are regarded as new aerial users, sharing the cellular spectrum with existing terrestrial users. However, with new characteristics and demands of aerial users, the severity of spectrum scarcity becomes more prominent and new spectrum management solutions are needed. In this paper, we consider a typical cUAM scenario where multiple aerial vehicles transport passengers/cargo along their pre-defined paths, with the coexistence of terrestrial users. We assume the minimum communication Quality of Service (QoS) must be achieved for all users at all times. Our objective is to simultaneously minimize aerial users' mission completion time and maximize terrestrial users' achievable data rate by jointly optimizing the spectrum allocation for all users and the moving velocities of aerial users. We formulate the optimization problem as a multi-stage Markov Decision Process and propose a multi-agent deep reinforcement learning-based algorithm. We also propose a heuristic greedy algorithm and an orthogonal multiple access algorithm as baseline solutions. Simulation results show that our learning-based solution outperforms the baseline solutions under different network configurations.

关键词： Simulation Heuristic algorithms Urban areas Quality of service Reinforcement learning Resource management Reliability

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Hardware-in-the-loop and Digital Twin Enabled Autonomous robotics-assisted Environment Inspection*

Hardware-in-the-loop and Digital Twin Enabled Autonomous Rob...

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International Symposium on Autonomous systems (ISAS)

作者： Johnny Li Bo Shang Iresh Jayawardana Genda Chen Department of Soil and Water Systems Precision Agriculture and Intelligent Robotics Laboratory University of Idaho Moscow USA Department of Electrical Engineering The City College of New York (CCNY /CUNY City College) New York NY USA Center for Intelligent Infrastructure Missouri University of Science and Technology Rolla USA

Empowered by the advanced 3D sensing, computer vision and AI algorithm, autonomous robotics provide an unprecedented possibility for close-up infrastructure environment inspection in an efficient and reliable fashion. Deep neural network (DNN) learning algorithms, pretrained on the large database can empower real-time object detection as well as fully autonomous, safe robotic navigation in unstructured environments while avoiding the potential obstacle. However, the development and deployment of the robots, inspection planning and operation procedures are still tedious and segmented with tremendous manual intervention during environmental inspection and anomaly monitoring. The proposed digital twin approach is able to provide a virtual representation model of the target environment either from a build-design or from 3D scanning of the real-world physical assets at high resolution in the Unity simulation environment, a transverse drone robot model and test its robotics Operating System(ROS) autonomous navigation and obstacle avoidance software stack, and the hardware-in-the-loop test can thus be conducted for the flight control algorithm effectiveness and real-time object detection performance evaluation. The preliminary result shows that VGG16-UNet deep learning algorithm was able to use only a small amount of guidance and time from experienced inspection pilots to successfully identify the critical elements and defects and real-time navigate around the unstructured environment. The proposed digital twin framework and methodology is promising to be utilized for developing and testing fully autonomous inspection robots and its path planning and navigation and detection operation with greater cost- and time-efficiency.

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