Ambiguous state evaluation of fuzzy algorithm based on fuzzy Petri nets

In order to represent a “human-like” behavior-decision ability in an autonomous mobile robot it is necessary to describe behavioral sequences while taking into account the recognition of ambiguous states. In our previous work we have already proposed such a macro behavior-decision algorithm based on a new interpretation of the concept of fuzzy algorithm that is capable of handling both crisp and fuzzy processings. In this paper we extend our previous work by using fuzzy Petri nets (FPN) to express a fuzzy algorithm. Ambiguous state transition is evaluated by marking changes of the fuzzy truth tokens. This new method assures that we can express the fuzzy algorithm in which explosions of fuzziness do not occur. For illustration purposes we present simulation results obtained using this method for the behavior-decision fuzzy algorithm for an autonomous mobile robot