Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach

Abstract
In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.

Citation
Khaldi B, Harrou F, Cherif F, Sun Y (2018) Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach. Biosystems. Available: http://dx.doi.org/10.1016/j.biosystems.2018.01.005.

Acknowledgements
This publication is based upon a collaboration work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582, and 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
Biosystems

DOI
10.1016/j.biosystems.2018.01.005

PubMed ID
29409799

Additional Links
http://www.sciencedirect.com/science/article/pii/S0303264717302897

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