Learning and adaptation of an intelligent mobile robot navigator operating in unstructured environment based on a novel online Fuzzy-Genetic system

Abstract In this paper we present our novel Fuzzy–Genetic techniques for the online learning and adaptation of an intelligent robotic navigator system. Such a system could be used by autonomous mobile vehicles navigating in unstructured and changing environments. In this work we focus on the online learning of the obstacle avoidance behaviour, which is an example of a behaviour that receives delayed reinforcement. We show how this behaviour can be co-ordinated with other behaviours that receive immediate reinforcement (such as goal seeking and edge following) learnt during our previous work to generate an intelligent reactive navigator that can deal with unstructured and changing outdoor environments. The system described uses a life long learning paradigm whereby it is able to dynamically adapt to new environments and update its knowledge base.

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