Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2019-CRG7-3800
Online Publication Date2019-07-23
Print Publication Date2019
Permanent link to this recordhttp://hdl.handle.net/10754/656274
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AbstractAggregation is a vital behavior when performing complex tasks in most of the swarm systems such as swarm robotics systems. In this paper, three new aggregation methods, namely the Distance-Angular, the Distance-Cosine, and the Distance-Minkowski k-nearest neighbor (k-NN) have been introduced. These aggregation methods are mainly built on well-known metrics: the Cosine, Angular and Minkowski distance functions, which are used here to compute distances among robots neighbors. Relying on these methods, each robot identifies its k nearest neighborhood set that will interact with. Then in order to achieve the aggregation, the interactions sensing capabilities among the set members are modeled using a virtual viscoelastic mesh. Analysis of the results obtained from the ARGoS simulator shows a significant improvement in the swarm aggregation performance while compared to the conventional distance-weighted k-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.
CitationKhaldi, B., Harrou, F., Cherif, F., & Sun, Y. (2019). Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation. IEEE Access, 7, 96372–96383. doi:10.1109/access.2019.2930677
SponsorsThe authors (Belkacem Khaldi and Foudil Cherif) would like to thank the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider,Biskra, Algeria for the continued support during the research. This work is supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800.