A sensory uncertainty field model for unknown and non-stationary mobile robot environments

A sensory uncertainty field (SUF) is a model of the localization uncertainty of a mobile robot. The value of the SUF at a specific robot configuration q expresses the expected uncertainty of the robot at q, as this would be measured by some localization procedure. Path planning over the SUF provides a way for better localization, and thus fewer failures, during navigation. In this paper we extend the original notion of a SUF to unknown and non-stationary environments. We propose a self-organizing neural network model that is capable of building and maintaining an estimation of the SUF while the robot moves around its free space, based on some dynamic localization information, e.g., Kalman filtering. The attractive feature of our algorithm is its capability of handling both unknown and dynamic, i.e., non-stationary, environments. We present a method for polygonal approximation of the resulting SUF by using the Delaunay triangulation