Adaptive data-driven error detection in swarm robotics with statistical classifiers

Lau, Hui Keng and Bate, Lain J. and Cairns, Paul A. and Timmis, John I. (2011) Adaptive data-driven error detection in swarm robotics with statistical classifiers. Robotics and Autonomous Systems, 59 (12). pp. 1021-1035. ISSN 0921-8890


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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.

Item Type: Article
Uncontrolled Keywords: Adaptive error detection, Statistical error detection, Swarm robotics
Subjects: ?? TJ210.2-211.47 ??
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: Unnamed user with email
Date Deposited: 12 Jul 2012 08:24
Last Modified: 17 Oct 2017 06:42

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