XPERSIF: a software integration framework & architecture for robotic learning by experimentation

  • The integration of independently-developed applications into an efficient system, particularly in a distributed setting, is the core issue addressed in this work. Cooperation between researchers across various field boundaries in order to solve complex problems has become commonplace. Due to the multidisciplinary nature of such efforts, individual applications are developed independent of the integration process. The integration of individual applications into a fully-functioning architecture is a complex and multifaceted task. This thesis extends a component-based architecture, previously developed by the authors, to allow the integration of various software applications which are deployed in a distributed setting. The test bed for the framework is the EU project XPERO, the goal of which is robot learning by experimentation. The task at hand is the integration of the required applications, such as planning of experiments, perception of parametrized features, robot motion control and knowledge-based learning, into a coherent cognitive architecture. This allows a mobile robot to use the methods involved in experimentation in order to learn about its environment. To meet the challenge of developing this architecture within a distributed, heterogeneous environment, the authors specified, defined, developed, implemented and tested a component-based architecture called XPERSIF. The architecture comprises loosely-coupled, autonomous components that offer services through their well-defined interfaces and form a service-oriented architecture. The Ice middleware is used in the communication layer. Its deployment facilitates the necessary refactoring of concepts. One fully specified and detailed use case is the successful integration of the XPERSim simulator which constitutes one of the kernel components of XPERO.The results of this work demonstrate that the proposed architecture is robust and flexible, and can be successfully scaled to allow the complete integration of the necessary applications, thus enabling robot learning by experimentation. The design supports composability, thus allowing components to be grouped together in order to provide an aggregate service. Distributed simulation enabled real time tele-observation of the simulated experiment. Results show that incorporating the XPERSim simulator has substantially enhanced the speed of research and the information flow within the cognitive learning loop.

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Metadaten
Document Type:Report
Language:English
Author:Iman Awaad, Beatriz Leon
Pagenumber:xiv, 183
ISBN:978-3-96043-001-8
ISSN:1869-5272
URN:urn:nbn:de:hbz:1044-opus-28
DOI:https://doi.org/10.18418/978-3-96043-001-8
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2008/02/01
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences, Department of Computer Science (02-2008)
GND Keyword:Robotik
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2010/03/10
Licence (Multiple languages):License LogoIn Copyright (Urheberrechtsschutz)