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Run-Time Compositional Software Platform for Autonomous NXT Robots

Run-Time Compositional Software Platform for Autonomous NXT Robots

Ning Gui, Vincenzo De Florio, Chris Blondia
Copyright: © 2011 |Volume: 2 |Issue: 2 |Pages: 14
ISSN: 1947-9220|EISSN: 1947-9239|EISBN13: 9781613505328|DOI: 10.4018/jaras.2011040103
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MLA

Gui, Ning, et al. "Run-Time Compositional Software Platform for Autonomous NXT Robots." IJARAS vol.2, no.2 2011: pp.37-50. http://doi.org/10.4018/jaras.2011040103

APA

Gui, N., De Florio, V., & Blondia, C. (2011). Run-Time Compositional Software Platform for Autonomous NXT Robots. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 2(2), 37-50. http://doi.org/10.4018/jaras.2011040103

Chicago

Gui, Ning, Vincenzo De Florio, and Chris Blondia. "Run-Time Compositional Software Platform for Autonomous NXT Robots," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 2, no.2: 37-50. http://doi.org/10.4018/jaras.2011040103

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Abstract

Autonomous Robots normally perform tasks in unstructured environments, with little or no continuous human guidance. This calls for context-aware, self-adaptive software systems. This paper aims at providing a flexible adaptive middleware platform to seamlessly integrate multiple adaptation logics during the run-time. To support such an approach, a reconfigurable middleware system “ACCADA” was designed to provide compositional adaptation. During the run-time, context knowledge is used to select the most appropriate adaptation modules so as to compose an adaptive system best-matching the current exogenous and endogenous conditions. Together with a structure modeler, this allows robotic applications’ structure to be autonomously (re)-constructed and (re)-configured. This paper applies this model on a Lego NXT robot system. A remote NXT model is designed to wrap and expose native NXT devices into service components that can be managed during the run-time. A dynamic UI is implemented which can be changed and customized according to system conditions. Results show that the framework changes robot adaptation behavior during the run-time.

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