Abstract: The paper proposes a robot zero calibration device and method based on spatial parameter clustering identification to overcome the shortcomings of traditional shaft pin and laser tracker calibration. The dial indicator and spatial analytic geometry are used to construct the zero calibration equation， which analyzes the robot’s theoretical post and posture values to obtain the zero-position training parameters. The clustering module identifies multiple sets of zero-position training parameters， and the optimal zero-position parameters of each joint can be solved. The experimental results show that spatial parameter clustering can improve the absolute accuracy by 72.9%， compared to the most commonly used shaft pin calibration method. Compared with the laser tracker calibration method with the highest accuracy， the time efficiency is improved by 87.5%. This method considers the calibration accuracy and efficiency and significantly improves the adaptability to the calibration environment.

Key words: clustering; industrial robot; parameter identification; zero calibration; machine learning