Behavior coordination of autonomous mobile robot navigation by neuro-fuzzy system

The complexity of an autonomous robot's navigation task, poses several roadblocks to the use of traditional fuzzy control schemes, such as much larger input space than typical fuzzy applications, adding inputs increases the required number of set evaluations exponentially, as the size of a rule base swells, manual description becomes difficult to impossible. Therefore in this paper a new behavior based navigation system is described. A serious problem in fuzzy behavior implementation is the requirement of multiple fuzzy rules, which results in multiple output recommendations. After studying different methods used for coordination of multiple behaviors and issues of conflict resolution among competing behaviors, it has been observed that the degrees of applicability are analogous to neuronal activation levels. Therefore, a novel neuro-fuzzy system is proposed for behavior integration, which results in a more accurate and optimal path.