Monitoring a robot swarm using a data-driven fault detection approach
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ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2017-06-30Online Publication Date
2017-06-30Print Publication Date
2017-11Permanent link to this record
http://hdl.handle.net/10754/625165
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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 BVJournal
Robotics and Autonomous SystemsAdditional Links
http://www.sciencedirect.com/science/article/pii/S0921889017300854ae974a485f413a2113503eed53cd6c53
10.1016/j.robot.2017.06.002