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Adaptive data-driven error detection in swarm robotics with statistical classifiers
University of York.
University of York.ORCID iD: 0000-0003-2415-8219
University of York.
University of York.
2011 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 59, no 12, 1021-1035 p.Article in journal (Refereed) Published
Abstract [en]

Swarm robotics is an example of a complex system with interactions among distributed autonomous robots as well with the environment. Within the swarm there is no centralised control, behaviour emerges from interactions between agents within the swarm. Agents within the swarm exhibit time varying behaviour in dynamic environments, and are subject to a variety of possible anomalies. The focus within our work is on specific faults in individual robots that can affect the global performance of the robotic swarm. We argue that classical approaches for achieving tolerance through implicit redundancy is insufficient in some cases and additional measures should be explored. Our contribution is to demonstrate that tolerance through explicit detection with statistical techniques works well and is suitable due to its lightweight computation.

Place, publisher, year, edition, pages
North-Holland , 2011. Vol. 59, no 12, 1021-1035 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23819DOI: 10.1016/j.robot.2011.08.008ISI: 000297180300001Scopus ID: 2-s2.0-80053560755OAI: oai:DiVA.org:mdh-23819DiVA: diva2:682355
Available from: 2013-12-27 Created: 2013-12-19 Last updated: 2014-06-19Bibliographically approved

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CiteExportLink to record
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