Robust distributed spatial clustering for swarm robotic based systems

This paper proposes and formally evaluates a distributed clustering strategy of swarm of robots into any predefined number of classes. The strategy is an extension of an existing work that is applicable for two classes only [9]. It is implemented and experimentally tested on a swarm of a real swarm of Kilobots. Based only on the local information coming from neighboring robots and the disposition of virtual tokens in the system, the robots of the swarm can be clustered into different classes. The proposed strategy acts in a distributed manner and without need of any global knowledge nor any movement of the robots. Depending on the amount and weight of the tokens available in the system, robots exchange information to reach a token homogeneous disposition. The clustering strategy is inspired by the settling process of liquids of different densities. Using information gathered from neighboring robots, a token density is computed. As a result, the tokens with higher weights cluster first, shifting those of lower weight, until they form differentiated bands for each group, thus completing the clustering process of the robots. (C) 2016 Elsevier B.V. All rights reserved.