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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 <inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-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 <inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-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 compared to the conventional distance-weighted <inline-formula> <tex-math notation="LaTeX">$\mathit {k}$ </tex-math></inline-formula>-NN aggregation method. Also, the aggregation performance of the methods is reported to be robust to partially faulty robots and accurate under noisy sensors.

Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation