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    Monitoring a robot swarm using a data-driven fault detection approach

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    1-s2.0-S0921889017300854-main.pdf
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    Type
    Article
    Authors
    Khaldi, Belkacem
    Harrou, Fouzi cc
    Cherif, Foudil
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    KAUST Grant Number
    OSR-2015-CRG4-2582
    Date
    2017-06-30
    Online Publication Date
    2017-06-30
    Print Publication Date
    2017-11
    Permanent link to this record
    http://hdl.handle.net/10754/625165
    
    Metadata
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    Abstract
    Using swarm robotics system, with one or more faulty robots, to accomplish specific tasks may lead to degradation in performances complying with the target requirements. In such circumstances, robot swarms require continuous monitoring to detect abnormal events and to sustain normal operations. In this paper, an innovative exogenous fault detection method for monitoring robots swarm is presented. The method merges the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average (EWMA) and cumulative sum (CUSUM) control charts to insidious changes. The method is tested and evaluated on a swarm of simulated foot-bot robots performing a circle formation task, via the viscoelastic control model. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed method where compared to the conventional PCA-based methods (i.e., T2 and Q).
    Citation
    Khaldi B, Harrou F, Cherif F, Sun Y (2017) Monitoring a robot swarm using a data-driven fault detection approach. Robotics and Autonomous Systems. Available: http://dx.doi.org/10.1016/j.robot.2017.06.002.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582. The work is done in collaboration with the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider, Biskra, Algeria. We would like to thank the reviewers of this article for their insightful comments, which helped us to greatly improve its quality.
    Publisher
    Elsevier BV
    Journal
    Robotics and Autonomous Systems
    DOI
    10.1016/j.robot.2017.06.002
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0921889017300854
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.robot.2017.06.002
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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