Applying Fuzzy Rule Interpolation for the Task of Controlling Guidance and Obstacle Avoidance Behaviour of a Robot

Several Fuzzy Rule Interpolation (FRI) techniques have limitations from the direct application point of view, for example their applicability is limited to the one dimensional case, or they can be defined only based on the two closest surrounding rules of the actual observation. This is the reason why relatively few FRI methods can be found among the practical fuzzy rule based applications. With the application of FRI methods sparse rule bases can be used, which substantially simplify the construction of fuzzy rule bases, because FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the gathered observation. Compared to the classical fuzzy CRI (compositional rule of inference), by eliminating the derivable rules, the number of the fuzzy rules needed in the rule base could be dramatically reduced. This paper provides a brief overview of several FRI methods and in more details an application oriented simple and quick FRI method “FIVE” will be introduced. For the demonstration of the benefits of the interpolation-based fuzzy reasoning as systematic approach, a robot guidance application is presented, where the robot is able to cycle through defined waypoints while avoiding collision with obstacles and walls. All of the controlling parts were accomplished with fuzzy rule bases of the “FIVE” FRI method.