Concurrent mapping and localization for mobile robot using soft computing techniques

This paper proposes a novel algorithm combining fuzzy logic (FL) and genetic algorithm (GA) for concurrent mapping and localization (CML) of mobile robot. First, CML is formulated as a multidimensional informed search problem. The search is performed to detect a robot pose which can best accommodate the recent sensor scan in the currently available map. A fuzzy set theoretic approach is used to predict a sample based representation of the state space of possible robot poses and a GA is designed to find out the globally optimal solution from the predicted pose space. The GA evaluates the fitness of poses based on the sensory information and drives the generation gradually towards the globally optimal solution even when the fuzzy prediction is inaccurate. The best fit solution as decided by GA offers the most likely continuation of the currently available map. Experiment on synthetic and real data illustrates the robustness of the algorithm.