Biologically inspired architecture for spatiotemporal learning of mobile robots

Abstract

Biological systems can adapt excellently to the demands of a dynamic world and changing tasks. What kind of information processing and reasoning do they use? There are numerous studies in psychology, cognitive neuroscience and artificial intelligence which complement each other and help in getting a better understanding of this riddle. Our paper presents a biologically inspired architecture for a spatiotemporal learning system. Multiple interconnected memory structures are used to incorporate different learning paradigms. Concurrent inherent learning processes complete the functionality of corresponding memory types. Our architecture has been evaluated in the context of mobile rescue robots: The task consists of searching objects while navigating in an unknown maze. © 2012 Springer-Verlag.