Optimal Search Strategy of Robotic Assembly Based on Neural Vibration Learning

Abstract EN

This paper presents implementation of optimal search strategy (OSS) in verification of assembly process based on neural vibration learning.

The application problem is the 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.

The 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 optimal search strategy based on minimal distance path between vibration parameter stage sets (amplitude and frequencies of robots gripe vibration) and recovery parameter algorithm, we can improve the robot assembly behaviour, that is, allow the fastest possible way of mating.

We have verified by using simulation programs that search strategy is suitable for the situation of unexpected events due to uncertainties.