Evolution of Task Switching Behaviors in Real Mobile Robots

Abstract

In recent years, much research is focused on evolution or learning of different behaviors. Because both algorithms require much computational time, most of these approaches are conducted in simulated environments. When evolution or learning took place in real robot, a single task was considered. In this work, we evolve neural controllers for task switching behavior using e-puck robots. The e-puck robot has to move to the sound source while first reaching the lights distributed in the environment. Experimental results show a good performance of neural controllers evolved in the real hardware of the e-puck robot.