This paper proposes and evaluates a clustering technique of swarm robots into
classes. Based only on the local information coming from neighboring robots and the distribution of virtual tokens in the system, the robots of the swarm can be grouped into different classes. The proposed technique acts in a distributed manner and without any global knowledge or movement of the robots. Depending on the amount and weight of the tokens available in the system, robots exchange information to reach a token uniform distribution. The clustering technique 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 form a cluster first, shifting those of lower weight, until they form differentiated bands for each group, thus completing the clustering of the robots.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten