A Method for Active Global Localization In Multi-robot System

In multi-robot system the ability to exchange information can reduce the uncertainty in the estimated location when robots can see each other. In this paper, a kind of dynamically evolving coordination architecture is proposed for cooperative localization according to the relative positions between robots. And to further improve the efficiency of cooperative localization, a decision theory based mechanism is proposed to make the robots cooperate actively during the localization process. Since stably tracking the multi-hypothesis of the robots' own position and their partners' position is of great importance for making a good decision of where to go in active localization, the co-evolution based adaptive Monte Carlo localization method in which samples are clustered into species to represents a hypothesis of robot's pose in a higher level than a single sample is adopted. Experiments are designed and carried out to prove the efficiency and stability of the proposed method.