Multiple sensors data integration using MFAM for mobile robot navigation

The mobile robot navigation with complex environment needs more input space to match the environmental data into robot outputs in order to perform realistic task. At the same time, the number of rules at the rule base needs to be optimized to reduce the computing time and to provide the possibilities for real time operation. In this paper, the optimization of fuzzy rules using a modified fuzzy associative memory (MFAM) is designed and implemented. MFAM provides good flexibility to use multiple input space and reduction of rule base for robot navigation. This paper presents the MFAM model to generate the rule base for robot navigation. The behavior rules obtained from MFAM model are tested using simulation and real world experiments, and the results are discussed in the paper and compared with the existing methods.