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Neural Dynamics for Cooperative Motion Control of Omnidirectional mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian Xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control Engineering Changchun University of TechnologyChangchun 130012China IEEE the College of Information Science and Engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

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Conservatism-Relaxed Disturbance Observer-based Composite Control for Wheeled mobile Robots Subject to Abrupt Disturbance

Conservatism-Relaxed Disturbance Observer-based Composite Co...

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Chinese Control and Decision Conference, CCDC

作者： Hao Wu Shuting Wang Hu Li Hongyang Zhang Tifan Xiong Yuanlong Xie School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China Department of Mobile Robotics Research and Technology Guangdong Intelligent Robotics Institute Dongguan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper investigates the robust and accurate control problem of wheeled mobile robot (WMR) systems with uncertain disturbance, especially exhibits abrupt changes. To end this, a conservatism-relaxed disturbance observer (CRDOB)-based sliding-mode controller is proposed under a composite control structure. To address the uncertainties of the abrupt disturbance, a CRDOB is designed without requiring any assumptions about disturbance boundary information. With aiding from a continuous gain-switching mechanism with reducing chattering of the SMC, a composite controller is developed to compensate for the residual-disturbance estimation error of the CRDOB, and then avoid large errors when disturbances instantaneous change. Simulations and experiments demonstrate the improvement of the tracking control performance and system robustness.

关键词： Couplings Adaptation models Estimation error Uncertainty Upper bound Prototypes Switches

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Cost-Aware Hierarchical Federated Learning via Over-the-Air Computing

Cost-Aware Hierarchical Federated Learning via Over-the-Air ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Donglin Xue Jingjing Luo Changkun Jiang Lin Gao School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China National Mobile Communications Research Laboratory Southeast University China Department of Computer Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781665435413

Federated Learning (FL) is a novel distributed learning framework to train the global model locally without collecting the raw data of clients. However, the performance of FL is greatly restricted by the limited network communication capacity between the cloud and clients. MEC-assisted Hierarchical Federated Learning (HFL) can effectively relieve the network pressure in FL, by transmitting and aggregating model parameters at the network edge based on the idea of mobile Edge Computing (MEC). The existing researches on HFL often adopt the traditional multiple access techniques (e.g., OFDMA) for the model transmission between clients and edge servers, which may be inefficient. In this work, we consider a novel Over-the-Air Computing (AirComp) based HFL framework, where clients send model parameters to edge servers simultaneously, and edge servers can directly complete the aggregation of model in the air by exploiting the superposition property of wireless channels. In such a scenario, we study the joint client association, transmission, and computation optimization problem, aiming at minimizing the overall energy consumption and latency. The problem is challenging due to the multi-level coupling between edge servers and clients. We decouple it into a client association subproblem and a resource optimization subproblem. We first show that the second subproblem is convex and can be easily solved by a coordinate descent algorithm. We then show that the first subproblem is a combinational optimization, and propose a near-optimal solution where each client is associated with the nearest edge server. Simulation results show that the AirComp-based HFL scheme outperforms the existing OFDMA-based schemes in terms of both energy consumption and latency.

关键词： Wireless communication Energy consumption Multi-access edge computing Federated learning Computational modeling Atmospheric modeling Simulation

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Detect-and-segment: A deep learning approach to automate wound image segmentation

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Informatics in Medicine Unlocked 2022年 29卷

作者： Scebba, Gaetano Zhang, Jia Catanzaro, Sabrina Mihai, Carina Distler, Oliver Berli, Martin Karlen, Walter Mobile Health Systems Lab Institute of Robotics and Intelligent Systems Department of Health Sciences and Technology ETH Zurich Switzerland Unit for Clinical and Applied Research Balgrist University Hospital Zurich Switzerland Department of Rheumatology University Hospital Zurich University of Zurich Switzerland Division of Technical Orthopedics Department of Orthopedic Surgery Balgrist University Hospital Zurich Switzerland

Chronic wounds significantly impact quality of life. They can rapidly deteriorate and require close monitoring of healing progress. Image-based wound analysis is a way of objectively assessing the wound status by quantifying important features that are related to healing. However, high heterogeneity of the wound types and imaging conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the perturbing background, and computed a wound segmentation map. We tested this approach on a diabetic foot ulcers data set and compared it to a segmentation method based on the full image. To evaluate its generalizability on out-of-distribution data, we measured the performance of the DS approach on 4 additional independent data sets, with larger variety of wound types from different body locations. The Matthews’ correlation coefficient (MCC) improved from 0.29 (full image) to 0.85 (DS) on the diabetic foot ulcer data set. When the DS was tested on the independent data sets, the mean MCC increased from 0.17 to 0.85. Furthermore, the DS enabled the training of segmentation models with up to 90% less training data without impacting the segmentation performance. The proposed DS approach is a step towards automating wound analysis and reducing efforts to manage chronic wounds. © 2022 The Authors

关键词： Chronic wounds Generalizability Machine learning Semantic segmentation Smartphone

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Detect-and-Segment: A deep learning approach to automate wound image segmentation

arXiv

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arXiv 2021年

Chronic wounds significantly impact quality of life. If not properly managed, they can severely deteriorate. Image-based wound analysis could aid in objectively assessing the wound status by quantifying important features that are related to healing. However, the high heterogeneity of the wound types, image background composition, and capturing conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the uninformative background, and computed the wound segmentation map. We evaluated this approach using one data set with images of diabetic foot ulcers. For further testing, 4 supplemental independent data sets with larger variety of wound types from different body locations were used. The Matthews’ correlation coefficient (MCC) improved from 0.29 when computing the segmentation on the full image to 0.85 when combining detection and segmentation in the same approach. When tested on the wound images drawn from the supplemental data sets, the DS approach increased the mean MCC from 0.17 to 0.85. Furthermore, the DS approach enabled the training of segmentation models with up to 90% less training data while maintaining the segmentation performance. © 2021, CC BY-NC-ND.

关键词： Diseases

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P17-01 Cell Painting unveils cell response signatures to (nano)particles formed in additive manufacturing

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Toxicology Letters 2022年 368卷 S226-S227页

作者： A. Alijagic N. Scherbak O. Kotlyar P. Karlsson A. Persson A. Hedbrant U. Norinder M. Larsson J. Felth L. Andersson E. Särndahl M. Engwall Örebro University Man-Technology-Environment research center (MTM) Örebro Sweden Örebro University Inflammatory Response and Infection Susceptibility Center (iRiSC) Örebro Sweden Örebro University School of Medical Sciences Örebro Sweden Örebro University Centre for Applied Autonomous Sensor Systems (AASS) Mobile Robotics and Olfaction Lab (MRO) Örebro Sweden Örebro University Department of Mechanical Engineering Örebro Sweden Uddeholms AB Hagfors Sweden Örebro University Hospital Department of Occupational and Environmental Medicine Örebro Sweden

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Editor-in-Chief

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IEEE Access 2024年 12卷 i-xi页

作者： Derek Abbott Sonia Aissa Anuradha Annaswamy Nirwan Ansari Jun Cai Kun-Shan Chen Yixin Chen J.-C. Chiao Ayman El-Baz Shaikh Fattah Hoay Gooi Maya Gokhale Chih-Lin Ⅰ Giuseppe Iannaccone Yumi Iwashita Bruce Jacob Abbas Jamalipour Bahram Javidi Weihua Jiang Okyay Kaynak Hulya Kirkici Germano Lambert-Torres Shengtao Li Michal Mrozowski Michele Nappi Dalma Novak Hugo Proença Yi Qian Zhihua Qu Susanto Rahardja Mehrdad Saif Ali Serpengüzel Peter H. Siegel Ashitey Trebi-Ollennu Daniela Tuninetti Ge Wang Keith A. Wear Andreas Weisshaar C. Patrick Yue Enrico Zio School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Institut National de la Recherche Scientifique (INRS) Québec City Canada Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Concordia University Montreal Canada Guilin University of Technology Guilin China Washington University St. Louis MO USA Southern Methodist University Dallas TX USA University of Louisville/Bioengineering Department Louisville KY USA Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Nanyang Technological University Jurong West Singapore Lawrence Livermore National Laboratory Livermore CA USA China Mobile Research Institute Beijing China University of Pisa Pisa Italy NASA-JPL California Institute of Technology Pasadena CA USA Florida Polytechnic University Lakeland FL USA The University of Sydney Sydney NSW Australia University of Connecticut Storrs CT USA Nagaoka University of Technology Nagaoka Niigata Japan Department of Electrical and Electronic Engineering Boğaziçi University Istanbul Turkey University of South Alabama Mobile AL USA Gnarus Institute Itajuba Brazil Xi’an Jiaotong University Xi’an China Gdansk University of Technology Gdansk Poland University of Salerno Fisciano Italy Octane Wireless Hanover MD USA University of Beira Interior Covilhã Portugal Department of Electrical and Computer Engineering University of NebraskaLincoln Lincoln NE USA University of Central Florida Orlando Florida USA Singapore Institute of Technology 10 Dover Dr Singapore Infocomm Technology Cluster 10 Dover Dr Singapore University of Windsor Windsor Canada Department of Physics Koç University Istanbul Turkey California Institute of Technology (CalTech) Pasadena California USA NASA-JPL Mobility and Robotics Systems S

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Wearable Flexible Hybrid Electronics: Advanced Soft Materials, Sensor Integrations, and Applications of Wearable Flexible Hybrid Electronics in Healthcare, Energy, and Environment (Adv. Mater. 15/2020)

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Advanced Materials 2020年第15期32卷

作者： Hyo‐Ryoung Lim Hee Seok Kim Raza Qazi Young‐Tae Kwon Jae‐Woong Jeong Woon‐Hong Yeo George W. Woodruff School of Mechanical Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta GA 30332 USA Department of Mechanical Engineering University of South Alabama Mobile AL 36608 USA Department of Electrical Computer & Energy Engineering University of Colorado Boulder Boulder CO 80309 USA School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea George W. Woodruff School of Mechanical Engineering Wallace H. Coulter Department of Biomedical Engineering Institute for Electronics and Nanotechnology Parker H. Petit Institute for Bioengineering and Biosciences Center for Flexible and Wearable Electronics Advanced Research Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA 30332 USA

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Opportunistic Radio Assisted Navigation for Autonomous Ground Vehicles

Opportunistic Radio Assisted Navigation for Autonomous Groun...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Hongkai Wen Yiran Shen Savvas Papaioannou Winston Churchill Niki Trigoni Paul Newman Department of Computer Science University of Oxford UK Singapore-MIT Alliance for Research and Technology Singapore Mobile Robotics Group University of Oxford UK

Navigating autonomous ground vehicles with visual sensors has many advantages - it does not rely on global maps, yet is accurate and reliable even in GPS-denied environments. However, due to the limitation of the camera field of view, one typically has to record a large number of visual experiences for practical navigation. In this paper, we explore new avenues in linking together visual experiences, by opportunistically harvesting and sharing a variety of radio signals emitted by surrounding stationary access points and mobile devices. We propose a novel navigation approach, which exploits side-channel information of co-location to thread up visually-separated experiences with short exploration phases. The proposed approach empowers users to trade travel time for manual navigation effort, allowing them to choose the itinerary that best serves their needs. We evaluate the proposed approach with data collected from a typical urban area, and show that it achieves much better navigation performance in both reach ability and cost, comparing with the state of the arts that only use visual information.

关键词： Vehicles Visualization Radio navigation Image segmentation Turning Cameras

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TRACKING CONTROL IN THE PRESENCE OF NONLINEAR DYNAMIC FRICTIONAL EFFECTS: ROBOT EXTENSION

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Asian Journal of Control 2008年第3期1卷

作者： M. Feemster D.M. Dawson A. Behal W. Dixon Matthew Feemster received the B.S degree in Electrical Engineering from Clemson University Clemson South Carolina in December 1994. Upon graduation he remained at Clemson University and received the M.S. degree in Electrical Engineering in 1997. During this time he also served as a research/teaching assistant. His research work focused on the design and implementation of various nonlinear control algorithms with emphasis on the induction motor and mechanical systems with friction present. He is currently working toward his Ph.D. degree in Electrical Engineering at Clemson University. Darren M. Dawson was born in 1962 in Macon Georgia. He received an Associate Degree in Mathematics from Macon Junior College in 1982 and a B.S. Degree in Electrical Engineering from the Georgia Institute of Technology in 1984. He then worked for Westinghouse as a control engineer from 1985 to 1987. In 1987 he returned to the Georgia Institute of Technology where he received the Ph.D. Degree in Electrical Engineering in March 1990. During this time he also served as a research/teaching assistant. In July 1990 he joined the Electrical and Computer Engineering Department and the Center for Advanced Manufacturing (CAM) at Clemson University where he currently holds the position of Professor. Under the CAM director's supervision he currently leads the Robotics and Manufacturing Automation Laboratory which is jointly operated by the Electrical and Mechanical Engineering departments. His main research interests are in the fields of nonlinear based robust adaptive and learning control with application to electro-mechanical systems including robot manipulators motor drives magnetic bearings flexible cables flexible beams and high-speed transport systems. Aman Behal was born in India in 1973. He received his Masters Degree in Electrical Engineering from Indian Institute of Technology Bombay in 1996. He is currently working towards a Ph.D in Controls and Robotics at Clemson University. His research focuses on the control of no

In this paper, we extend the observer/control strategies previously published in [25] to an n -link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects modeled by the Lu-Gre model. In addition, we also present a new adaptive control technique for compensating for the nonlinear parameterizable Stribeck effects. Specifically, an adaptive observer/controller scheme is developed which contains a feedforward approximation of the Stribeck effects. This feedforward approximation is used in a composite controller/observer strategy which forces the average square integral of the position tracking error to an arbitrarily small value. Experimental results are included to illustrate the performance of the proposed controllers.

关键词： Friction compensation nonlinear control

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