Abstract

The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot. The evolutionary approach tothe development of neural controllers for autonomous agents has been successfully used by many researchers, but most-if not all- studies have been carried out with computer simulations. Instead, in this research the whole evolutionary process takes places entirely on a real robot without human intervention. Although the experiments described here tackle a simple task of navigation and obstacle avoidance, we show a number of emergent phenomena that are characteristic of autonomous agents. The neural controllers of the evolved best individuals display a full exploitation of non-linear and recurrent connections that make them more e cient than analogous man-designed agents. In order to fully understand and describe the robot behavior, we have also employed quantitative ethological tools [13], and showed that the adaptation dynamics conform to predictions made for animals. 1

Citations