A framework for defining and learning fuzzy behaviors for autonomous mobile robots

Abstract

In this paper we show our work on the use of fuzzy behaviors in the field of autonomous mobile robots. We address here how we use learning techniques to efficiently coordinate the conflicts between the different behaviors that compete with each other to take control of the robot. We use fuzzy rules to perform such fusion. These rules can be set using expert knowledge, but as this can be a complex task, we show how to automatically define them using genetic algorithms. We also describe the working environment, which includes a custom programming language (named BG) based on the multi-agent paradigm. Finally, some results related to simple goods-delivery tasks in an unknown environment are presented. © 2002 John Wiley & Sons, Inc.