Robust Force-Controlled Robotic Insertion Skills for Geometrically Diverse Parts Under Uncertainty

Abstract

This paper presents a generic model-based methodology for defining and executing robust force-based insertion skills capable of handling geometric uncertainties in robotic assembly tasks with sub-millimiter clearance fits. The approach can deal with geometric variability (i.e. different object shapes), insertions with multiple contact points such as dual pegs and complex mating features such as snap-fit assemblies. Robustness is achieved through a sequence of task-space degree-of-freedom reductions, one at a time, each executed via force-based control and monitoring to systematically reduce uncertainties. In addition, the paper introduces an algorithm that automatically generates insertion skills for planar polygonal objects, which are commonly found in industrial applications such as printed circuit board (PCB) assembly and quality control. The methodology is validated through extensive experiments involving objects of varying geometries and tight sub-millimeter clearance fits.