Neural Network Based Object Classifier with Fuzzy Filtered Data for Mobile Robot Navigation

抄録

Object identification, classification and modelling in unknown or known environments have become an interesting research topic. In this paper, a fusion algorithm combining fuzzy reasoning and neural network is presented for object classification in mobile robot navigation. The imprecise information generated by sonar range finders are filtered and normalized by using Tsukamoto type fuzzy inference system. Then the filtered range information and the pre-processed image data are fused through a back propagation type feedforward neural network to classify the objects in the world environment. Some experimental results show the effectiveness of the proposed methodology.