Path Correction for Paint Spraying Based on 3D Vision

To enhance adaptability in automated racket manufacturing, this study presents an intelligent deviation correction system for robotic paint spraying. Traditional robotic spraying systems follow fixed paths, which can lead to incomplete coatings due to positional or angular shifts of rackets during production line transfer. To address this limitation, the proposed system integrates a six-axis robotic arm with a 3D machine vision module. A structured-light 3D camera is employed to capture the real-time pose of each racket, including its rotation angles along multiple axes. Based on the measured deviations, the system dynamically adjusts the spraying trajectory to align with the actual racket position. Experimental results demonstrate that the proposed system significantly improves coating accuracy and coverage consistency under varying positional conditions, thereby increasing the reliability and flexibility of the spraying process.