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IEEE Conference on Cybernetics and Intelligent Systems

作者： Chee Khiang Pang Cao Vinh Le Oon Peen Gan Xiang Min Chee Dan Hong Zhang Ming Luo Hian Leng Chan Frank L. Lewis Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology ASTAR (Agency for Science Technology & Research) Singapore Automation & Robotics Research Institute University of Texas Arlington Fort Worth TX USA

To reduce energy consumption for sustainable and energy-efficient manufacturing, a good understanding of the dynamic energy consumption patterns on the manufacturing shop floor is essential. In this paper, we introduce a novel approach to address the challenge of missing operation context information during in-situ energy data measurement. Finite-State Machines (FSMs) are used to model the engineering processes, and a two-stage framework for online classification of real time energy measurement data in terms of machine operation states is proposed for energy audit and machine management. The first stage uses advanced signal processing techniques to reduce noise while preserving important features, and the second stage uses intelligent pattern recognition algorithms to cluster energy consumption patterns. Our proposed two-stage framework is evaluated on an industrial injection moulding system using a Savizky-Golay (SG) filter and a Neural Network (NN), and our experimental results show a 95.85% accuracy in identification of machine operation states.

关键词： Energy consumption Artificial neural networks Injection molding Power demand Monitoring Energy measurement

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WORKSHOP: The role of expectations in intuitive human-robot interaction

WORKSHOP: The role of expectations in intuitive human-robot ...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Verena Hafner Manja Lohse Joachim Meyer Yukie Nagai Britta Wrede Department of Computer Science Cognitive Robotics Berlin Germany CoR-Laboratory and Applied Informatics Technical Faculty Bielefeld University Germany Department of Industrial Engineering and Management University of Negev Israel Department of Adaptive Machine Systems Graduate School of Engineering Osaka University Japan Head of CoR-Laboratory research group Hybrid Society Technical Faculty Bielefeld University Germany

Human interaction is highly intuitive: we infer reactions of our opponents mainly from what we have learned in years of experience and often assume that other people have the same knowledge about certain situations, abilities, and expectations as we do. In human-robot interaction (HRI) we cannot take for granted that this is equally true since HRI is asymmetrical. In other words, robots have different abilities, knowledge, and expectations than humans. They need to react appropriately to human expectations and behaviour. With this respect, scientific advances have been made to date for applications in entertainment and service robotics that largely depend on intuitive interaction. However, HRI today is often still unnatural, slow, and unsatisfactory for the human interlocutor. Both the sensorimotor interaction with environment and interlocutor, and the social aspects of the interaction still need to be researched and improved. Therefore, this full-day workshop aims to bring together researchers from different scientific fields to discuss these crosscutting issues and to exchange views on what are the preconditions and principles of intuitive interaction.

关键词： Educational institutions computer science Robot sensing systems Electronic mail Humans Industrial engineering

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Special issue on approximate dynamic programming and reinforcement learning

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Journal of Control Theory and Applications 2011年第3期9卷 309-309页

作者： Ferrari, Silvia Sarangapani, Jagannathan Lewis, Frank L. Department of Mechanical Engineering and Materials Science Duke University Box 90300 Durham NC 27708-0005 United States Department of Electrical and Computer Engineering University of Missouri-Rolla Missouri-Rolla MO 65401 United States Automation and Robotics Research Institute The University of Texas at Arlington 7300 Jack Newell Blvd. S Ft. Worth TX 76118-7115 United States

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Welcome from the technical program chairs

Welcome from the technical program chairs

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Jadwiga Indulska Claudio Bettini Roy Campbell Cecilia Mascolo Centro E. Piaggio and the Department of Information Engineering University of Pisa Pisa Italy Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA “Soft Robotics for Human Cooperation and Rehabilitation” Lab Istituto Italiano di Tecnologia Genoa Italy

A warm welcome to the Ninth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom 2011). We are very excited to introduce you to the technical program of our conference that this year includes 27 papers representing high-quality research conducted over the broad spectrum of topics related to pervasive computing.

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Decentralized Task Sequencing and Multiple Mission Control for Heterogeneous Robotic Networks

Decentralized Task Sequencing and Multiple Mission Control f...

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2011 IEEE International Conference on robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

作者： Donato Di Paola Andrea Gasparri Giovanni Ulivi David Naso Frank L. Lewis Institute of Intelligent Systems for Automation (ISSIA) National Research Council (CNR)70126 BariI Faculty of Engineering Computer Science and Automation DepartmentUniversity "Roma Tre"00146 RomeI Department of Electrical and Electronic Engineering (DEE) Polytechnic of Bari70126 BariItaly the Automation and Robotics Research Institute University of Texas at ArlingtonFort WorthTX 76118-

In this paper a novel decentralized approach for task sequencing within a multiple missions control framework is presented. The main contribution of this work concerns the decentralization of a control framework for multiple mission execution in order to enhance the robustness of the system, and the application of the latter to a heterogeneous robotic network. The proposed approach is based on the Matrixbased Discrete Event Framework (MDEF). This formalism is adapted to networks of heterogeneous robots, i.e., robots with different capabilities, and to the decentralized control of mission execution using a consensus-based approach which guarantees the agreement among robots on executed actions and their consequences.

关键词： Mathematical model Equations Resource management Robot sensing systems Availability Multiagent systems

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Recovery of occluded face uisng direct combined model-based particle filter

Recovery of occluded face uisng direct combined model-based ...

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2010 International Conference on System science and engineering, ICSSE 2010

作者： Tu, Ching-Ting Lien, Jenn-Jier James Department of Computer Science and Information Engineering Robotics Laboratory National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781424464746

In this paper, we present a Bayesian framework for recovering the occluded facial image without the aid of manual face alignment or user-specified occlusion region. The proposed Bayesian framework unifies the recovery stage with face alignment and occlusion detection, and such complex probability distribution is represented by a particle set. Into this framework, each particle is one possible solution of the recovered face which is composed of several patches. First, the occluded facial patches of each particle are detected, and then are recovered by inferring their local facial details from other non-occluded patches. Further, by including the global facial geometry as a constraint, the recovered results are robust to the local image noise which then cause the alignment parameters are accurately calculated. Particularly, we also propose a novel direct combined model (DCM)-based particle filter that utilizes the face specific prior knowledge to perform such particle-based solution efficiently and robustly. Our extensive experiment results demonstrate that the recovered images are quantitatively closer to the ground truth without manual involvement. © 2010 IEEE.

关键词： Principal component analysis

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A robotics engineering M.S. degree

A robotics engineering M.S. degree

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2010 ASEE Annual Conference and Exposition

作者： Gennert, Michael Michalson, William Demetriou, Michael Computer Science Department Robotics Engineering Program Worcester Polytechnic Institute United States Worcester Polytechnic Institute United States

There is an increasing demand for creative scientific, engineering, and management talent to meet national needs. We believe that the best way to educate professionals for leadership roles is in a unified multi-disciplinary curriculum. This paper describes one such program, a Master of science degree in robotics engineering at Worcester Polytechnic Institute, whose goal is to prepare men and women for technical leadership in the robotics industry and research in robotics. The program, launched in fall 2009, develops competence in electro-mechanical-computational systems and an awareness of management systems. It constitutes a multi-disciplinary effort by faculty in Arts & sciences (Mathematics and computer science), engineering (Electrical & computer engineering and Mechanical engineering), and Management. The program is organized around a technical coursework core with select science and engineering electives. Technical strength is supplemented by organizational and management principles embodied in context courses in Systems engineering and Management. The program culminates in individual capstone projects that take the form of a thesis, directed research, or practicum with written report. In all cases, a public oral presentation is required. A B.S./M.S. option allows the program to be completed in a single additional year of work past the Bachelor's degree. By using existing courses where appropriate, we were able to start the program by developing only two new courses, Foundations of robotics and Robot Dynamics, supplemented by special topics, independent study, and other project work. We anticipate adding new courses as the program grows, additional faculty join the program, and as we introduce a Ph.D. in robotics engineering. Initial response to the program by students and industry has been very positive, and the program shows great promise to contribute significantly to meeting future engineering workforce needs. © American Society for engineering Education,

关键词： Curricula

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Service-oriented context reasoning incorporating patterns and knowledge for understanding human-augmented situations

Service-oriented context reasoning incorporating patterns an...

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19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010

作者： Lim, Gi Hyun Kim, Kun Woo Chung, Byoungjun Suh, Il Hong Suh, Hyowon Kim, Munsang Department of Electronics and Computer Engineering Hanyang University Korea Republic of Department of Industrial Engineering KAIST Daejeon Korea Republic of Center for Intelligent Robotics Korea Institute of Science and Technology Seoul Korea Republic of

ISBN: (纸本)9781424479917

Understanding situations has been regarded as a highly difficult task due to its complexity, especially in case of human-augmented ones where the contexts of situations are largely influenced by human activity. Even though the complexity of situations can be solved by modeling the real environment, it is not easy to model which covers uncertain problems of real world effectively. For this, this paper proposes a fusion technology for service-oriented context reasoning combining low-level sensory patterns and high-level semantic knowledge using Hidden Markov Model (HMM) and ontology. Integrated temporal reasoning using our approach enables a service robot to understand human-augmented situations immediately whenever the critical situation happens. Experimental results show that the proposed method can successfully extract situations from continuous sensory signals with 80% percent reliability. © 2010 IEEE.

关键词： Hidden Markov models

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On challenges of robot assisted radiotherapy for lung tumors

On challenges of robot assisted radiotherapy for lung tumors

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2010 IEEE International Conference on robotics and Biomimetics, ROBIO 2010

作者： Ma, Lei Herrmann, Christian Schilling, Klaus School of Electrical Engineering Southwest Jiaotong University Chengdu 610031 China University of Würzburg Department of Computer Science VII: Robotics and Telematics D-97074 Würzburg Germany

ISBN: (纸本)9781424493173

This paper is concerned with recent development in robot assisted radiotherapy for lung tumors. The paper first analyzes technical challenges regarding tumor position determination and prediction, by invasive and non-invasive methods, correlation models of the tumor motion and the respiratory signals, as well as modeling and control of the robot manipulator. Several currently available solutions and concepts are then briefly introduced. Emphasis is placed on the ATTS (adaptive tumor tracking system) system developed at the University of Würzburg. Within the ATTS, information of the tumor motion is used to control a robotic treatment couch HexaPOD, which moves in six degrees of freedom and maintains the tumor in a certain fixed spatial position. With this new technology it is possible to avoid gating during the radiotherapy as well as active breathing control, such that duration and cost of the therapy as well as trauma of the patient can be reduced. The tumor position is obtained by data fusion from two sensing system. Correlation methods for data fusion were extensively investigated and a prediction model for tumor motion is found that can forecast the tumor position for up to one respiratory period. Modeling of the HexaPOD treatment couch is addressed. The most suitable model is found to be a low-order linear dynamic equation together with a saturation component. Three different control methods and comparative test results are then presented to show feasibility of the proposed model and control schemes. © 2010 IEEE.

关键词： Radiotherapy

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Preface

Communications and Control Engineering

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Communications and Control engineering 2011年第9780857292551期 vii-xi页

作者： Sun, Zhendong Ge, Shuzhi Sam College Automation Science & Engineering Center for Control and Optimization South China University of Technology Guangzhou510640 China Robotics Institue and Institute of Intelligent Systems and Information Technology University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering The National University of Singapore Singapore Singapore

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