Minimum Distance Localization for a Robot with Limited Visibility

Abstract

Minimum distance localization is the problem of finding the shortest possible path for a robot to eliminate ambiguity regarding its position in the environment. We consider the problem of minimum distance localization in self-similar environments, where the robot's sensor has limited visibility, and describe two randomized algorithms that solve the problem. Our algorithms reduce the risk of requiring impractical observations and solve the problem without excessive computation. Our results are validated using numerical simulations.