Flexible and Efficient Topological Approaches for a Reliable Robots Swarm Aggregation

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.