Spatial Exploration, Map Learning, and Self-Positioning with MonaLysa

Abstract

This paper describes how the MonaLysa control architecture implements a route-following navigation strategy. Two procedures that allow map building and self-positioning are described, and experimental results are provided that demonstrate that such procedures are robust with respect to noise. This approach is compared to others with similar objectives, and directions for future work are outlined. 1 Introduction In robotics or animat research, traditional navigation methods that use internal geometrical representations of the environment (Latombe, 1991) are confronted with various implementation difficulties, due to memory and time requirements, as well as sensory and motor errors (Nehmzow, 1995). To overcome these difficulties, several researchers (Chatila and Laumond, 1985; Kuipers and Byun, 1991; Mataric, 1992; Nehmzow, 1995) have advocated the use of various types of topological models to represent the connectivity of the environment, and several such models have been devised that...