Opinion Dynamics over a Finite Set in Cooperative Multi-robot Systems: An Asynchronous Gossip-Based Consensus Approach
The focus of this paper is to present an algorithm that allows robotic teams to make decisions between a finite set of choices. The approach used was based on models that represent the way groups of humans evolve their opinions through time. Numerous works have explored models that consider the opinion as continuous values, while the literature less frequently considers groups trying to reach an agreement when only a finite set of possible opinions is given. The main contribution of this paper is to present a consensus algorithm that can be applied in those scenarios. For this purpose, it is briefly reviewed some crucial concepts for the definition of the proposed algorithm, which is based on asynchronous gossip. Due to the stochasticity of this approach, it is not possible to precisely predict the behavior of the network. However, the results from both computational and laboratory experiments indicate the eigenvector centrality score as a valuable metric to predict the probability of an initial opinion to become the prevailing one for the group when they reach consensus. Also, the asynchrony of the proposed algorithm made it possible to reach consensus in scenarios where synchronous approaches could not.