A robust exploration and navigation approach for indoor mobile robots merging local and global strategies

Abstract

A mobile robot must explore its workspace in order to learn a map of its environment. Given the perceptual limitations and accuracy of its sensors, the robot has to stay close to obstacles in order to track its position and never get lost. This paper describes a new method for exploring and navigating autonomously in indoor environments. It merges a local strategy, similar to a wall following strategy to keep the robot close to obstacles, within a global search frame, based on a dynamic programming algorithm. This hybrid approach takes advantages of local strategies that consider perceptual limitations of sensors without losing the completeness of a global search. These methods for exploring and navigating are tested using a mobile robot simulator with very good results. © Springer-Verlag Berlin Heidelberg 2000.