A hybrid autonomous robot navigation method based on artificial intelligence and soft computing techniques

The main problem to be solved for autonomous indoor robots is that the environment where they have to move safety is only partially known and possibly dynamically changing. In this paper a hybrid navigation method is proposed, using two techniques that deal with a priori information and sensory data separately, thus blending the intelligence and optimality of global navigation methods with the reactivity and low complexity of local ones. The first, global navigation module, based on a priori information, chooses intermediary goals for the local navigation module, for which the so called A* algorithm is used. The second part, carrying out the (local) navigation relying on sensory data, applies a fuzzy-neural representation of an improved potential field based guiding navigation tool. Vision based obstacle detection is implemented by difference detection based on a combination of RGB and HSV representations of the pixels. Copyright (C) 2003 IFAC.Int Federat Automat Control, IEEE, Int Fuzzy Syst Assoc, Neural Networks So