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检索条件"机构=Computational Robotics Lab in the Department of Computer Science"

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Using the modular controller architecture (mca) in ambient assisted living

Using the modular controller architecture (mca) in ambient a...

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3rd IET International Conference on Intelligent Environments, IE'07

作者： Koch, Jan Anastasopoulos, Michalis Berns, Karsten Robotics Research Lab Department of Computer Science University of Kaiserslautern Gottlieb-Daimler-Strae Kaiserslautern 67663 Germany Fraunhofer Institute for Experimental Software Engineering Fraunhofer-Platz 1 Kaiserslautern 67663 Germany

ISBN: (纸本)9780863418532

The concept of ambient intelligence requires software architectures enabling machine intelligence in unstructured dynamic environments. Research in mobile robotics tries to address this field since many years and has successfully created several architectural concepts for behaviorbased reactive systems. We describe the architectural and communication-oriented capabilities of the Modular Controller Architecture (MCA) and how ambient intelligence systems may benefit from these. Especially the shared memory approach of MCA offers a great possibility to facilitate distributed dynamic applications. MCA has been developed for mobile robotics research, initially at the FZI in Karlsruhe1 [11] and is under continuous development2. MCA addresses primitive and object-related communication, re-usability, component-based development, hardware access and application distribution. We want to take a closer look on the MCA framework and how it is already in use within an assisted living environment, established during several projects at Fraunhofer IESE [9] [2] Several sensors and a mobile service unit were provided by the robotics Research lab3 and the MCA framework was used for service interconnection. An intelligent refrigerator, two human localisation technologies and the mobile service robot ARTOS have MCA running as described in section 3. We present our experiences with the Modular Controller Architecture regarding the parameters identified by the ubiquitous community [13]. The outline of this paper is as follows: First the architectural approaches in other projects are mentioned. The Modular Controller Architecture is described in detail, with focus on the requirements of ambient intelligence. We continue by describing our assisted living demonstrator and the services and applications relevant to this work. With reference to our example imple-mentation we conclude the features of MCA that comply to the requirements. Furthermore it is described which future work shall lead to

关键词： Middleware

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A biologically inspired approach for the control of the hand

A biologically inspired approach for the control of the hand

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Congress on Evolutionary Computation

作者： Emmanouil Hourdakis Michail Maniadakis Panos Trahanias Computational Vision and Robotics Laboratory Foundation for Research and Technology Hellas Heraklion Greece Department of Computer Science University of Crete Greece Postdoctoral researcher with the Computational Vision and Robotics Laboratory Foundation for Research and Technology Hellas Heraklion Greece Head of the Computational Vision and Robotics Laboratory Foundation for Research and Technology Hellas Heraklion Greece Professor with the Department of Computer Science University of Crete Greece

The control of the hand in primate species is characterized by a high dimensionality, due to the large number of joints in the fingers. In this study we present how its manipulation can be simplified without compromising its usage, through a constraint methodology that is inspired from recent neurobiological findings. We further develop a computational model, consisting of several brain areas related to hand control, using a co-evolutionary architecture. Due to its neurobiological basis the methodology gives rise to a number of emergent properties that have been shown to occur in primate species during reach-to-grasp tasks.

关键词： Biological control systems Brain modeling computer science computer architecture Biological system modeling computational modeling Neurons Biological neural networks computer vision Robot kinematics

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The second international conference on Human-Robot Interaction

The second international conference on Human-Robot Interacti...

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作者： Schultz, Alan C. Breazeal, Cynthia Fong, Terry Kiesler, Sara Washington DC United States Department of Media Arts and Scienes MIT Media Lab. Intelligent Robotics Group NASA Ames Research Center Department of Computer Science and Human Computer Interaction Carnegie Mellon University Pittsburgh PA United States

The second international conference on Human-Robot Interaction (HRI-2007) was held in Arlington, Virginia, March 9-11, 2007. The theme of the conference was "Robot as Team Member" and included posters and paper presentations on teamwork, social robotics, adaptation, observation and metrics, attention, user experience, and field testing. One hundred seventy-five researchers and practitioners attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2008 will be held in Amsterdam, The Netherlands from March 12-15, 2008. Copyright © 2007, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： robotics

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A topology-independent similarity measure for high-dimensional feature spaces

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17th International Conference on Artificial Neural Networks, ICANN 2007

作者： Kerdels, Jochen Peters, Gabriele DFKI German Research Center for Artificial Intelligence Robotics Lab. Robert Hooke Str. 5 D-28359 Bremen Germany University of Dortmund Department of Computer Science Computer Graphics Otto-Hahn-Str. 16 D-44221 Dortmund Germany

ISBN: (纸本)9783540746935

In the field of computer vision feature matching in high dimensional feature spaces is a commonly used technique for object recognition. One major problem is to find an adequate similarity measure for the particular feature space, as there is usually only little knowledge about the structure of that space. As a possible solution to this problem this paper presents a method to obtain a similarity measure suitable for the task of feature matching without the need for structural information of the particular feature space. As the described similarity measure is based on the topology of the feature space and the topology is generated by a growing neural gas, no knowledge about the particular structure of the feature space is needed. In addition, the used neural gas quantizes the feature vectors and thus reduces the amount of data which has to be stored and retrieved for the purpose of object recognition. © Springer-Verlag Berlin Heidelberg 2007.

关键词： computer vision

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Hands-Off Therapist Robot Behavior Adaptation to User Personality for Post-Stroke Rehabilitation Therapy

Hands-Off Therapist Robot Behavior Adaptation to User Person...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Adriana Tapus Cristian Tapus Maja J Mataric Robotics Research Lab/Interaction Lab Department of Computer Science University of Southern California Los Angeles USA Mojave Research Lab Department of Computer Science California Institute of Technology Pasadena USA

This paper describes a hands-off therapist robot that monitors, assists, encourages, and socially interacts with post-stroke users in the process of rehabilitation exercises. We developed a behavior adaptation system that takes advantage of the users introversion-extroversion personality trait and the number of exercises performed in order to adjust its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward a customized post-stroke rehabilitation therapy. The experimental results demonstrate the robot's autonomous behavior adaptation to the user's personality and the resulting user improvements of the exercise task performance.

关键词： Rehabilitation robotics Medical treatment Human robot interaction robotics and automation computer science Pediatrics Adaptive systems Helium Cognitive robotics Senior citizens

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An efficient method for protein secondary structure prediction

An efficient method for protein secondary structure predicti...

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2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

作者： Wu, Tao Mao, Junjun Zhang, Ling State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210093 China School of Mathematics and Computational Science Anhui University Hefei 23009 China Key Lab IC and SP Anhui University Hefei 230039 China

ISBN: (纸本)1424411203

The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming. In this paper, a constructive machine learning approach is used to predict protein secondary structure with five different encoding schemes, the results show that the constructive algorithm can achieve high predicting accuracies and the encoding schemes have influence on predicting result. © 2007 IEEE.

关键词： Proteins

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2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kai Welke Erhan Oztop Gordon Cheng Rudiger Dillmann IAIM Institute of Computer Science and Engineering (CSE) Karlsruhe Germany JST Computational Brain Project ICORP Kawaguchi Saitama Japan Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Soraku-gun Kyoto Japan

A cognitive robot system has to acquire and efficiently store vast knowledge about the world it operates in. To cope with every day tasks, a robot needs to learn, classify and recognize a manifold of different objects. Our work focuses on an object representation scheme that allows storing perceived objects in a compact way. This will enable the system to store extensive information about the world and will ease complex recognition tasks. The human visual system deploys several mechanisms to reduce the amount of information. Our goal is to develop an artificial system that mimics these mechanisms to create representations that can be used in cognitive tasks. In particular, in this paper we will present an approach that exploits similarities among different views of objects. The proposed representation scheme allows for reduction of storage required for the representation of objects and preserves the information about the similarity among objects. This is achieved by selecting 'important views' of objects, depending on their stability. Furthermore, by extending the same approach to multiple objects, we are able to exploit similarities between objects to find a common representation and to further reduce the storage requirements.

关键词： Cognitive robotics Humanoid robots Intelligent robots Humans Robot sensing systems Object recognition USA Councils computer science Visual system

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Synthesizing goal-directed actions from a library of example movements

Synthesizing goal-directed actions from a library of example...

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IEEE-RAS International Conference on Humanoid Robots

作者： Ales Ude Marcia Riley Bojan Nemec Andrej Kos Tamim Asfour Gordon Cheng Jožýef Stefan Institute Department of Automatics Biocybernetics and Robotics Ljubljana Slovenia Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kyoto Japan College of Computing Georgia Institute of Technology Atlanta GA USA Institute of Computer Science and Engineering c/o Technologiefabrik University of Karlsruhe Karlsruhe Germany Japan Science and Technology Agency ICORP Computational Brain Project Kawaguchi Saitama Japan

We present a new learning framework for synthesizing goal-directed actions from example movements. The approach is based on the memorization of training data and locally weighted regression to compute suitable movements for a large range of situations. The proposed method avoids making specific assumptions about an adequate representation of the task. Instead, we use a general representation based on fifth order splines. The data used for learning comes either from the observation of events in the Cartesian space or from the actual movement execution on the robot. Thus it informs us about the appropriate motion in the example situations. We show that by applying locally weighted regression to such data, we can generate actions having proper dynamics to solve the given task. To test the validity of the approach, we present simulation results under various conditions as well as experiments on a real robot.

关键词： Libraries Humanoid robots Physics computing Hidden Markov models Orbital robotics robotics and automation Cybernetics laboratories computer science Data engineering

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PCA-based Least Squares Support Vector Machines in Week-Ahead Load Forecasting

PCA-based Least Squares Support Vector Machines in Week-Ahea...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： M. Afshin A. Sadeghian Computational Intelligence Initiative (C12) lab Department of Electrical and Computer Engineering Ryerson University Toronto ONT Canada Department of Computer Science Ryerson University Canada

Week-ahead load forecasting is essential in the planning activities of every electricity production and distribution company. This paper proposes the application of principal component analysis (PCA) to least squares support vector machines (LS-SVM) in a week-ahead load forecasting problem. New realistic features are added to better and more efficiently train the model. For instance, it was found that hours of daylight are influential in shaping the load profile. This is particularly important in case of cities that are situated in the northern hemisphere. To show the effectiveness, the introduced model is being trained and tested on the data of the historical load obtained from Ontario's independent electricity system operator (IESO) for the Canadian metropolis, Toronto. Analysis of the experimental results proves that LS-SVM by feature extraction using PCA can achieve greater accuracy and faster speed than other models including LS-SVM without feature extraction and the popular feed forward back-propagation neural network (FFBP) model.

关键词： Least squares methods Support vector machines Load forecasting Principal component analysis Feature extraction Production planning Cities and towns System testing Feeds Neural networks

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An efficient algorithm for scheduling problem with batching and common due window

An efficient algorithm for scheduling problem with batching ...

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2007 International Conference on computational Intelligence and Security Workshops, CIS 2007

作者： Hongluan, Zhao Guojun, Li Jiqiang, Wang Guoyong, Han School of Computer Science and Technology Shandong Jianzhu Univ. Jinan China School of Mathematics and System Science Shandong Univ. Jinan China Computational Systems and Biology Lab. Department of Biochemistry and Molecular Biology Univ. of Georgia United States School of Statistics and Mathematics Shandong Finance Inst. Jinan China

ISBN: (纸本)0769530737

Jobs share a common due window and belong to distinct families with sequence-independent setup tasks on a single machine. The window location and size are decision variables with linear penalties. Our objective is to find a schedule minimizing the sum of the weighted number of early and tardy jobs, together with the determination cost of the due window. Following several properties of an optimal schedule, a polynomial algorithm is firstly proposed and range it to P-problem. © 2007 IEEE.

关键词： Scheduling algorithms

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