Optimization Through Coupling Learning Capability And Imitation Strategy In A Multi-Robot System

In this paper, we present the interest of coupling learning capability and imitation strategy on individual and population levels in the field of Multi-Robot System. Particularly, we show that in an unknown environment adding a simple imitation capability to our bio-inspired architecture leads to a positive effect in the improvement the overall performance of the whole system. Indeed, our motivations is to optimize the robots goals discovery time and to improve the survival rate of agents. To analyze and validate our hypothesis, a series of experiments has been performed with and without a low level imitation strategy in a simulated multi-robot system. We will conclude with robotics' experiments which will feature how our approach applies accurately to real life environments.