Safety of Autonomous Evolutionary Robots : Elimination of Certain Behavioral patterns by Complex Systems Analysis

Abstract

Evolutionary robotics is a useful technique for the automatic creation of autonomous robots. However, to use them in actual environment, we have to solve a problem that they might occur the unexpected behavior in phase period during evolution. In this paper, we describe a new method of artificial evolution with which autonomous robot acquires safe and flexible actions. Analysis of sensory information of robots under various conditions revealed that the certain pattern between robot’s behavior and the flow of sensory information, and this pattern is unique under each condition. By memorizing such patterns during move in the maze field and learning a pattern that lead to crash to the wall, the robot obtains an ideal action evolutionally without clashing to the wall even in phase period during evolution. Thus, this method may have an advantage for evolution of the robot without occurring the unexpected dangerous behavior.