by Alexander Scheidler, Arne Brutschy, Eliseo Ferrante, and Marco Dorigo
October 2011
In this paper we propose a collective decision making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the shortest mean execution time. A central aspect of the proposed method is the fact that consensus on the fastest action emerges from the initially heterogeneous beliefs of the robots. We study two analytical models of the proposed decision making method to understand the dynamics of the consensus formation process. Moreover, we verify the applicability of the method in a real swarm robotics scenario. To this end, we conduct three sets of experiments that show that a robotic swarm can collectively select the shortest of two paths. Finally, we use a Monte Carlo simulation model to study and predict the influence of different parameters on the accuracy of the method.
