A compensation method based on extreme learning machine to enhance absolute position accuracy for aviation drilling robot. (March 2018)

• Record Type: Journal Article
• Authors: Yuan, Peijiang; Chen, Dongdong; Wang, Tianmiao; Cao, Shuangqian; Cai, Ying; Xue, Lei
• Is Part Of: Advances in mechanical engineering. Volume 10:Number 3(2018)
• Publication Date: 2018-03
• Subjects: Robot compensation -- absolute positional error -- positional error prediction -- extreme learning machine -- drilling robot; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814018763411
• Languages: English
• ISSNs: 1687-8132

Abstract

To enhance the absolute position accuracy and solve complex modeling and computational complexity problems in traditional compensation methods for aviation drilling robots, a compensation method based on the extreme learning machine model was proposed in this article. The proposed method, in which the influence of geometric factors and the non-geometric factors of the robot is considered, builds a positional error prediction model based on extreme learning machine. As the input and output training data, the theoretical position and positional errors measured by a high-precision laser tracker were used to train and construct the extreme learning machine model. After the extreme learning machine model was constructed, the positional errors of prediction points could be predicted using the trained extreme learning machine. Then, the drilling robot controller could be directed to compensate for the predicted positional errors. To verify the correctness and effectiveness of the method, a series of experiments were performed with an aviation drilling robot. The experimental results showed that choosing an appropriate number of training points and hidden neurons for extreme learning machine could increase the computational efficiency without decreasing the high absolute position accuracy. The results also show that the average and maximum absolute position accuracy of robot tool center point were improved by 75.89% and 80.93%, respectively.