Learning of Robotic Assembly based on Specially Adjustable Vibrations Parameters

Abstract

This paper investigates the use of autonomous learning in the problems of complex robot assembly of miniature parts in the example of mating the gears of one multistage planetary speed reducer. Assembly of tube over the planetary gears was noticed as the most difficult problem of overall assembly and favourable influence of vibration and rotation movement on compensation of tolerance was also observed. With the proposed neural network based learning algorithm, it is possible to find extended scope of vibration state parameter. Using deterministic search strategy based on minimal distance path action between vibration parameter stage sets and recovery parameter algorithm, we can improve the robot assembly behaviour, i.e. allow the fastest possible way of mating.