Progressive construction of compound behavior controllers for autonomous robots using temporal information

Abstract

In this work we present a methodology for the progressive construction of compound behavior controllers for real autonomous robots. Some of these behaviors require temporal processing which is achieved through the inclusion of temporal delays in the synapses of the artificial neural networks used for their implementation. Starting from a set of simple behaviors implemented by means of evolved monolithic controllers, the evolution strategy employed obtains behaviors in higher levels either choosing the necessary low level behaviors from the previously selected set or through the coevolution of part of the low level behaviors and the higher level one. Emphasis is placed on making the behaviors robust and capable of performing in a real robot.