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Nonlinear Model Predictive Formation Control for Quadcopters

IFAC-PapersOnLine 2015年第19期48卷 39-44页

作者： Ribeiro, Tiago T. Conceição, André G.S. Sa, Inkyu Corke, Peter LaR - Robotics Laboratory Department of Electrical Engineering Federal University of Bahia Salvador Brazil Australian Centre for Robotic Vision School of Electrical Engineering and Computer Science Queensland University of Technology Australia

This paper presents a Nonlinear Model Predictive Formation Control for a group of Quadcopters. In this approach the controllers are distributed to the quadcopters and the coupling is done through its objective function. The path following problem is expressed through a dynamic model of the quadcopter's state errors relative to a Serret-Frenet frame, and the coordination problem is solved through adjustments in the robots' speeds combined with the path update rate for each robot. A strategy based on constraints relaxing is proposed to solve the problem of high state and formation errors, factors that directly affect feasibility and stability. Simulation results considering a realistic scenario are provided to demonstrate the performance of the proposed control strategy. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Problem solving Model predictive control Nonlinear systems Predictive control systems Unmanned aerial vehicles (UAV) Control strategies Coordination problems Formation control Nonlinear model predictive control Objective functions Path following problems Realistic scenario Serret Frenet frame

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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User-adaptive fall detection for patients using wristband

User-adaptive fall detection for patients using wristband

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Young-Hoon Nho Jong Gwan Lim Dae-Eon Kim Dong-Soo Kwon Robotics Program and Human-Robot Interaction research center Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Information Engineering Computer Science and University of Trento Italy KAIST Human-Robot Interaction Research Center Daejeon Republic of Korea Department of Mechanical Engineering Human-Robot Interaction research center Daejeon Republic of Korea

Fall detection systems have been proposed to prevent additional injuries following fall accidents. This paper introduces an easily learnable fall detection system based on the data of an individual patient in a hospital room. The improvement of low performance using a single accelerometer at wrists and the inconvenience of sensor attached to a waist in the conventional approach was concentrated on by integrating heart rate signals to the conventional acceleration approach and changing the sensor location from a waist to wrists. As for the optimal heart rate feature selection, we proposed a four-feature vector combination (root mean square of successive differences, standard deviation of successive differences, normal to normal 50, normal to normal 20) with correlation and mutual information analysis in addition to mean absolute deviation selected as an accelerometer feature. To easily acquire and train the patients' fall data, our system was based on unsupervised learning approaches using Gaussian mixture models for optimal classifiers with the optimal cluster number decided by cluster validation index of square error sum. A 10-fold cross validation was applied for a final performance evaluation where each threshold for separating fall state from non-fall state was automatically decided in several comparison groups, which were created on the basis of fusion timing and used sensors. As a result, despite sensors attached to the wrist, the wearable inconvenience of the conventional is overcome using the feature-level fused approach between heart rates and accelerations with the accuracy up to 98.39 %, which is closest to 99.34 % of the case using a single accelerometer located at the waist.

关键词： Accelerometers Heart rate Wrist Training data Hospitals Robot sensing systems Unsupervised learning

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Knowledge engineering with Big Data

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IEEE Intelligent Systems 2015年第5期30卷 46-55页

作者： Wu, Xindong Chen, Huanhuan Wu, Gongqing Liu, Jun Zheng, Qinghua He, Xiaofeng Zhou, Aoying Zhao, Zhong-Qiu Wei, Bifang Gao, Ming Li, Yang Zhang, Qiping Zhang, Shichao Lu, Ruqian Zheng, Nanning School of Computer Science and Information Engineering Hefei University of Technology China University of Vermont United States University of Edinburgh School of Computer Science United Kingdom School of Computer Science University of Science and Technology of China China Department of Computer Science Xi'An Jiaotong University China Institute for Data Science and Engineering East China Normal University China Pennsylvania State University United States Fudan University China Department of Computer Science Zhejiang Gongshang University China Deakin University Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China Keio University Japan

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. © 2001-2011 IEEE.

关键词： Intelligent systems

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Guest-editorial computer-based intelligent technologies for improving the quality of life

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IEEE Journal of Biomedical and Health Informatics 2015年第1期19卷 4-5页

作者： Mozos, Oscar Martinez Galindo, Cipriano Tapus, Adriana School of Computer Science University of Lincoln LincolnLN6 7TS United Kingdom System Engineering and Automation Department University of Malaga Malaga29071 Spain Robotics and Computer Vision Lab ENSTA-ParisTech Palaiseau91120 France

The eight papers in this special section focus on computer-based intelligent technologies for improving the quality of life.

关键词： Special issues and sections Electroencephalography Collision avoidance Assistive technology Mobile radio mobility management Robot monitoring Brain-computer interfaces

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Nadzoru: A Software Tool for Supervisory Control of Discrete Event Systems

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IFAC-PapersOnLine 2015年第7期48卷 182-187页

作者： Pinheiro, Lucas Preischadt Lopes, Yuri Kaszubowski Leal, André Bittencourt Rosso Junior, Roberto Silvio Ubertino Department of Computer Science Santa Catarina State University Joinville-SC Brazil Department of Automatic Control and Systems Engineering Sheffield Robotics University of Sheffield Sheffield South Yorkshire United Kingdom Department of Electrical Engineering Santa Catarina State University Joinville-SC Brazil

This article presents a software tool for the synthesis of supervisory control based on untimed finite state automata. The tool has a friendly graphical interface for the creation of models and, among other features, allows: automata simulation;analysis of problems related to the implementation of the control logic;and code generation for multiple devices. The code generation is based on a template system that allows the easy inclusion of support for new devices. Currently the tool is able to generate code for some microcontrollers and programmable logic controllers. Another feature of the tool is the capability of being easily extended with new algorithms and modules. © 2015

关键词： Discrete event simulation

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Iterative Closest Labeled Point for tactile object shape recognition

Iterative Closest Labeled Point for tactile object shape rec...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Shan Luo Wenxuan Mou Kaspar Althoefer Hongbin Liu Centre for Robotics Research Department of Informatics King's College London WC2R 2LS UK School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS UK

ISBN: (纸本)9781509037636

Tactile data and kinesthetic cues are two important sensing sources in robot object recognition and are complementary to each other. In this paper, we propose a novel algorithm named Iterative Closest Labeled Point (iCLAP) to recognize objects using both tactile and kinesthetic information. The iCLAP first assigns different local tactile features with distinct label numbers. The label numbers of the tactile features together with their associated 3D positions form a 4D point cloud of the object. In this manner, the two sensing modalities are merged to form a synthesized perception of the touched object. To recognize an object, the partial 4D point cloud obtained from a number of touches iteratively matches with all the reference cloud models to identify the best fit. An extensive evaluation study with 20 real objects shows that our proposed iCLAP approach outperforms those using either of the separate sensing modalities, with a substantial recognition rate improvement of up to 18%.

关键词： Three-dimensional displays Feature extraction Shape Measurement Robot sensing systems Object recognition

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Author Correction: Zebrafish as an animal model in epilepsy studies with multichannel EEG recordings

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Scientific reports 2017年第1期7卷 18112页

作者： Sung-Joon Cho Donghak Byun Tai-Seung Nam Seok-Yong Choi Byung-Geun Lee Myeong-Kyu Kim Sohee Kim School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology (GIST) Gwangju 61005 Republic of Korea. School of Mechanical Engineering Gwangju Institute of Science and Technology (GIST) Gwangju 61005 Republic of Korea. Department of Neurology Chonnam National University Medical School Gwangju 61469 Republic of Korea. Department of Biomedical Sciences Chonnam National University Medical School Gwangju 61469 Republic of Korea. Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea. soheekim@dgist.ac.kr.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

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Oximetry: Miniaturized Battery‐Free Wireless Systems for Wearable Pulse Oximetry (Adv. Funct. Mater. 1/2017)

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Advanced Functional Materials 2017年第1期27卷

作者： Jeonghyun Kim Philipp Gutruf Antonio M. Chiarelli Seung Yun Heo Kyoungyeon Cho Zhaoqian Xie Anthony Banks Seungyoung Han Kyung‐In Jang Jung Woo Lee Kyu‐Tae Lee Xue Feng Yonggang Huang Monica Fabiani Gabriele Gratton Ungyu Paik John A. Rogers Department of Materials Science and Engineering Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Departments of Materials Science and Engineering Northwestern University Evanston IL 60208 USA Beckman Institute University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Departments of Biomedical Engineering Northwestern University Evanston IL 60208 USA Department of Electrical and Computer Engineering Frederick Seitz Materials Research Laboratory University of Illinois at Urbana‐Champaign Urbana IL 61801 USA Department of Civil and Environmental Engineering Northwestern University Evanston IL 60208 USA AML Department of Engineering Mechanics Center for Mechanics and Materials Tsinghua University Beijing 100084 China Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 South Korea Departments of Civil and Environmental Engineering Mechanical Engineering Materials Science and Engineering Center for Engineering and Health Skin Disease Research Center Northwestern University Evanston IL 60208 USA Department of Energy Engineering Hanyang University Seoul 133‐791 Republic of Korea Departments of Materials Science and Engineering Biomedical Engineering Chemistry Neurological Surgery Mechanical Engineering Electrical Engineering and Computer Science Simpson Querrey Institute & Feinberg Medical School Center for Bio‐Integrated Electronics Northwestern University Evanston IL 60208 USA

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Nonlinear Model Predictive Formation Control for Quadcopters

Nonlinear Model Predictive Formation Control for Quadcopters

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关键词： Unmanned aerial vehicles (UAV)

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