Aggregation 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.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2019-CRG7-3800
Acknowledgements: The 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.