A self-learning automaton with variable resolution for high precision assembly by industrial robots

Abstract

This paper reports on the use of the stochastic automaton theory to configure control algorithms for high precision assembly operations performed with a force-sensing robot. The basic principle of the stochastic automation, i.e., its variable structure, has been extended to the dimensionality of the automaton by gradually optimizing the resolution of the input variables.