A Sensory Uncertainty Field Model for Unknown andNon - stationary Mobile Robot

A Sensory Uncertainty Field (SUF) is a model of the localization uncertainty of a mobile robot. The value of the SUF at a speciic robot connguration 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 ltering. 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.