FACTORS AFFECTING THE ACCURACY AND EFFICIENCY OF ROBOT MANIPULATORS IN AUTOMATED SURFACE PAINTING

Abstract

This study investigates key factors influencing accuracy and efficiency in robotic surface painting applications. Through experimental analysis and modeling, we identify critical parameters affecting paint quality and application uniformity, including trajectory planning, kinematic configurations, end-effector design, and environmental conditions. Results demonstrate that optimized path planning algorithms reduced coating thickness variation by 28%, while advanced control systems minimized positional errors by 74% compared to conventional methods. The findings provide valuable insights for improving automated painting systems in industrial applications, particularly in automotive and aerospace manufacturing sectors where surface finish quality is crucial.