An Efficient Error Compensation Method for Milling Robot Based on Transfer Learning

doi:10.3901/JME.2022.14.170

Abstract

Abstract: The grid compensation is one of the effective methods to improve the positioning accuracy of industrial robot. However, error measurement is time-consuming due to the many sampling robot configurations required. To improve positioning error compensation efficiency, proposes a mechanism analysis and data-driven method for milling robot, which predict the positioning error of different regions in the robot workspace based on transfer learning. Firstly, the rigid-flexible coupling error model of the robot is established, and the similarity of the error distribution in different regions of the cube and cylindrical workspace is analyzed. Then, the robot workspace is divided into source domain and target domain by considering error similarity of different areas. Complete robot sampling configurations and errors measurement data are obtained by hierarchical sampling method as source data for the source workspace, and only a few robot configurations are required to measure the positioning errors as the target data for targe regions. The source domain data and target domain data are used to train the Gaussian process regression model (GPR). The prediction accuracy of the transfer learning model is improved by subspace alignment and adaptive weight method based on weighted fitting error. The robot positioning error is predicted and compensated according to the robot positions. Finally, a KR160 milling robot is used for error compensation experiments to verify the feasibility and effectiveness of the proposed method. The experimental results prove that the robot positioning error is reduced from 1.499 mm to 0.182 mm after compensation, and the number of robot sampling poses is reduced by 70%, the contour error and position error of the flange hole are 0.269 mm and 0.331 mm, which proves that the method can improve the compensation efficiency and machining accuracy.

Key words: positioning errors, transfer learning, milling robot, compensation efficiency, coupling error model

CLC Number:

TP242

DENG Kenan, GAO Dong, MA Shoudong, LU Yong. An Efficient Error Compensation Method for Milling Robot Based on Transfer Learning[J]. Journal of Mechanical Engineering, 2022, 58(14): 170-180.

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