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Samenvatting

General Purpose Service Robots operate in different environments of a dynamic nature. Even the robot's programmer cannot predict what kind of failure conditions a robot may confront in its lifetime. Therefore, general purpose service robots need to efficiently handle unforeseen failure conditions. This
requires the capability of handling unforeseen failures while the robot is performing a task. Existing research typically offers special-purpose solutions depending on what has been
foreseen at the design time. In this research, we propose a general purpose argumentation-based architecture which is able to autonomously recover
from unforeseen failures. We compare the proposed method with existing incremental online learning methods in the literature. The results show that the proposed argumentation-based learning approach is capable of learning complex scenarios with higher precision than other methods.
Originele taal-2English
TitelInternational Conference on Automation Science and Engineering (CASE) 2019
UitgeverijIEEE Computer Society
Hoofdstuk8843207
Pagina's1699-1704
Aantal pagina's6
ISBN van elektronische versie9781728103556
ISBN van geprinte versie9781728103563
DOI's
StatusPublished - 1-aug-2019
Evenement15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duur: 22-aug-201926-aug-2019

Publicatie series

NaamIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN van geprinte versie2161-8070
ISSN van elektronische versie2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Land/RegioCanada
StadVancouver
Periode22/08/201926/08/2019

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