Robotic mapping of friction and roughness for reality-based modeling

Abstract

This paper discusses the robotic acquisition and characterization of surface friction and roughness for real-world objects. Our motivation is the construction of detailed "reality-based" models for existing objects that can be used in haptic displays and other applications involving simulation. A key challenge addressed in this paper is the acquisition of these surface properties on real objects with nontrivial shape, and registration of these properties with respect to geometric models of the shape. We show how Coulomb friction may be effectively estimated in the presence of variations in surface normal. We also show how to estimate a stochastic process model of surface roughness. Finally, we demonstrate the robotic mapping of surface friction over an entire object, using the UBC Active Measurement Facility (ACME).