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RRA: Models and tools for robotics run-time adaptation

  • Robotics applications are characterized by a huge amount of variability. Their design requires the developers to choose between several variants, which relate to both functionalities and hardware. Some of these choices can be taken at deployment-time, however others should be taken at run-time, when more information about the context is known. To make this possible, a software system needs to be able to reason about its current state and to adapt its architecture to provide the configuration that best suites the context. This paper presents a model-based approach for run-time adaptation of robotic systems. It defines a set of orthogonal models that represent the system architecture, its variability, and the state of the context. Additionally it introduces a set of algorithms that reason about the knowledge represented in our models to resolve the run-time variability and to adapt the system architecture. The paper discusses and evaluates the approach by means of two case studies.

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Metadaten
Document Type:Conference Object
Language:English
Author:Luca Gherardi, Nico Hochgeschwender
Parent Title (English):IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015). Hamburg, Germany, Sept. 28 2015-Oct. 2 2015
First Page:1777
Last Page:1784
DOI:https://doi.org/10.1109/IROS.2015.7353608
Publication year:2015
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:2016/03/03