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World Congress on Intelligent control and Automation

作者： Pei-Feng Qi Qing-Hao Meng Ya-Qi Jing Ming Zeng Shu-Gen Ma Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems Tianjin University Department of Robotics College of Science and Engineering Ritsumeikan University

ISBN: (纸本)9781467384155

We designed a portable electronic nose (e-nose) for rapid detection of Chinese liquors. The e-nose had an evaporation chamber wrapped with silicone heating band, which could vaporize the liquor samples in 1 minute. The sequential sampling and clean-out lasted 100 seconds and 3 minutes, respectively. Hence, one fast detection process could be finished within 3 minutes without regard to clean-out. In order to acquire enough information from array's sampling curves for classification, we chose 60 features (including 6 features per sensor) according to the response curves, the derivative of the response curves and the relative change rate curves of conductivity. Then the dimension of features was reduced by principle component analysis (PCA). Finally, classification of Chinese liquors was performed using C-Support Vector Machine (C-SVM) and the classification rate was 96.7%.

关键词： Sensor arrays Conductivity Principal component analysis Gas detectors Heating Support vector machine classification Support vector machine classification Electronic Nose Gas detectors Sensor arrays principal components analysis conductivity response curves change rate Heating Chinese Chinese Chinese philosophy Chinese art

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Generalized Norm Optimal Iterative Learning control with Intermediate Point and Sub-interval Tracking

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International Journal of Automation and computing 2015年第3期12卷 243-253页

作者： David H.Owens Chris T.Freeman Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield School of Electronics and Computer Science University of Southampton Advanced Robotics Division Italian Institute of Technology

Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.

关键词： Iterative learning control learning control optimization linear systems robotics.

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Flexible robotic arm dynamics through deflection screw approach

Flexible robotic arm dynamics through deflection screw appro...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Mariapaola D'Imperio Luca Carbonari Myrel Alsayegh Torsten Bertram Matteo Claudio Palpacelli Darwin Caldwell Ferdinando Cannella Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Industrial Engineering and Mathematical Sciences Universit Politecnica delle Marche Ancona Italy Institute of Control Theory and Systems Engineering Technische Universitat Dortmund Dortmund Germany

ISBN: (纸本)9781509061914

The design and control of robotic systems capable of performing dynamic tasks, such as those involving stable impact, is of growing interest within the robotic community. If their structure is conceived as rigid, it results in massive and bulky systems, so that high forces are generated in the interaction with the external environment. At the opposite, if the robotic structure is designed as flexible, the interaction with the external environment produces lower forces thanks to their weight reduction and to the energy absorption guaranteed by the structural deformations. However, adding flexibility leads to unwanted vibrations which is a major drawback, that must be taken into account by the control algorithm to prevent resonance phenomena and to guarantee precise task performance. In this paper, the authors propose a structural model of a flexible robotic arm to be implemented in a model based control algorithm. The study is based on the screw theory approach which allows for an easy-to-manipulate concise symbolic representation for both kinematics and dynamics.

关键词： Fasteners Service robots Manipulators Kinematics Aerodynamics Heuristic algorithms

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A tactile-based fabric learning and classification architecture

A tactile-based fabric learning and classification architect...

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International Conference on Information and Automation for Sustainability (ICIAfS)

作者： A.A. Khan M. Khosravi S. Denei P. Maiolino W. Kasprzak F. Mastrogiovanni G. Cannata Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy Computing Department University of London London United Kingdom Institute of Control and Computation Eng. Warsaw University of Technology Warszawa Poland

ISBN: (纸本)9781509061334

This paper proposes an architecture for tactile-based fabric learning and classification. The architecture is based on a number of SVM-based learning units, which we call fabric classification cores, specifically trained to discriminate between two fabrics. Each core is based on a specific subset of the fully available set of features, on the basis of their discriminative value, determined using the p-value. During fabric recognition, each core casts a vote. The architecture collects votes and provides an overall classification result. We tested seventeen different fabrics, and the result showed that classification errors are negligible.

关键词： Fabrics Feature extraction Sensor phenomena and characterization Tactile sensors Frequency modulation Frequency-domain analysis

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Variable stiffness mechanisms using spherical continuously variable transmissions

Variable stiffness mechanisms using spherical continuously v...

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ASME 2015 International Mechanical engineering Congress and Exposition, IMECE 2015

作者： Capehart, Twan Moore, Carl A. Center for Intelligent Systems Control and Robotics Department of Mechanical Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States

ISBN: (纸本)9780791857397

Some robot developers are considering elasticity to provide compliance for better adaption to a changing environment, shock resistance and safer human-robotic interactions (HRI). In this study we simulate a spherical continuously variable transmission (CVT) to validate its ability as the primary mechanism in a variable stiffness device for a 1D robotic hopper. The spherical CVT has been used in many robotic applications including cobots by Peshkin et al. [1] and as the driving unit in the load sensitive mechanism by Tadakuma et al. [2]. A CVT based variable stiffness/damping device intended for vibration mitigation was presented by Little [3]. That paper presented the kinematics and experiments of the CVT based variable stiffness/damping device for the specific application of vibration mitigation. This study considers the dynamics of the CVT based variable stiffness/damping device and modifies the device based on previous studies. We use ADAMS to simulate the modified system because it captures many of the real world dynamics arising from the CVT's rolling friction dynamics. Finally we present a conceptual design of the variable stiffness CVT and briefly discuss its use in a 1D legged hopper application. Copyright © 2015 by ASME.

关键词： Spheres

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control laws development for a free-flying unmanned robotic system to support interplanetary bodies prospecting and characterization missions

Control laws development for a free-flying unmanned robotic ...

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AIAA Guidance, Navigation, and control Conference, 2016

作者： Perez, Andres E. Moncayo, Hever Prazenica, Richard J. Zacny, Kris Mueller, Robert P. Ebert, Tom Dupuis, Michael Flight Dynamics and Control Research Laboratory Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Aerospace Engineering Department Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Exploration Technology Group Honneybee Robotics Company CA United States Exploration Research and Technology Programs Science and Technology Projects Division NASA Kennedy Space Center FL United States Autonomous Flight Systems Lead Testing and Design Branch NASA Kennedy Space Center FL United States Robotics and Autonomous Systems Testing and Design Branch NASA Kennedy Space Center FL United States

ISBN: (数字)9781624103896

ISBN: (纸本)9781624103896

In situ Resource Utilization (ISRU) facilitates planetary exploration by drawing needed resources, such as water, from the local environment. However, the extreme nature of these environments require the development of advanced unmanned space systems integrated with sample-capture devices to achieve the ultimate goal of prospecting these resources. This paper presents the design, development and Hardware-in-the-Loop (HIL) simulation testing of guidance and tracking control laws for an autonomous small marsupial free-flyer prospector system. The control laws are based on an extended non-linear dynamic inversion (NLDI) approach and its implementation is illustrated through HIL simulation using a mathematical model of an autonomous vehicle research platform developed by NASA Kennedy Space Center. This vehicle has been designed to support the development, testing and validation of algorithms for safe, reliable, and scalable control space missions with minimal need for human intervention in complex, unstructured environments. The main objective of the control laws is to minimize 3-axis distances with respect to a desired trajectory and maintain stability and adequate performance in the presence of uncertainties. The performance of the control laws is evaluated during autonomous flight in terms of trajectory tracking errors, real-time execution on board the flight computer, and control activity at nominal and dynamically-changing conditions. The results show that for all mission cases investigated the control laws approach has desirable capabilities and is reliable for in-flight testing operation as a next step towards the validation and verification of this configuration. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Hardware-in-the-loop simulation

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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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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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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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Representations of body schemas for infant robot development

Representations of body schemas for infant robot development

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IEEE International Conference on Development and Learning, ICDL

作者： Patricia Shaw James Law Mark Lee Department of Computer Science Aberystwyth University Sheffield Robotics & Department of Automatic Control and Systems Engineering The University of Sheffield

Psychological studies have often suggested that internal models of the body and its structure are used to process sensory inputs such as proprioception from muscles and joints. Within robotics, there is often a need to have an internal representation of the body, integrating the multi-modal and multi-dimensional spaces in which it operates. Here we propose a body model in the form of a series of distributed spatial maps, that have not been purpose designed but have emerged through our experiments on developmental stages using a minimalist content-neutral approach. The result is an integrated series of 2D maps storing correlations and contingencies across modalities, which has some resonances with the structures used in the brain for sensorimotor coordination.

关键词： Neck Robot sensing systems Robot kinematics Visualization Modeling Joining processes

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