A study on vibration suppression strategies for welding robots based on Kalman filtering and Bayesian optimization

Purpose This paper aims to propose a novel methodology to mitigate welding torch vibration caused by sensor measurement noise during laser vision-based seam tracking, which compromises robotic welding quality. Design/methodology/approach A Kalman filter is applied to smooth the noisy sensor data for precise tracking. To avoid costly and inefficient manual tuning of the filter’s hyperparameters, Bayesian optimization is utilized to approximate optimal settings with minimal experimental iterations