A Fuzzy-Evolutionary Algorithm for Simultaneous Localization and Mapping of Mobile Robots

This paper presents a real world application of fuzzy logic and Genetic algorithm (GA) in mobile robotics. It proposes a novel method of integrating fuzzy logic and GA to solve the Simultaneous Localization And Mapping (SLAM) problem of mobile robots. The proposed algorithm, termed as Fuzzy-Evolutionary SLAM, solves the global optimization problem of SLAM where the objective function measures the quality of a robot´s pose in accommodating a local map into a partially developed global map of the environment. The search for the optimal robot´s pose is performed by a GA. Knowledge on the problem domain is preprocessed by a fuzzy logic system and allows the GA to evolve within a specified region of the search space. It helps to speed-up the GA based search. The proposed algorithm processes data in an incremental fashion and follows essentially no assumption about the environment. Experimental results validate the performance of the proposed algorithm.