A Compensation Method for Enhancing Aviation Drilling Robot Accuracy Based on Co-Kriging
- 4 Downloads
The positional error of aviation drilling robot has a great influence on the strength and lives of aircrafts in the aircraft assembly. In order to improve the position accuracy of aviation drilling robot, an error compensation method based on co-kriging is proposed in this paper. The error similarity based on the kinematic of drilling robot is proposed firstly. Then, the positional errors of a set of points in the workspace are measured by using precision laser tracker. The measurement data are used to fit the cross-variogram of positional error. After the cross-variogram is obtained, the predicted positional errors of verification points can be estimated based on co-kriging. The positions after compensation are given to the drilling robot. The proposed method is carried out on an aviation drilling robot for practical compensation to verify the correctness and effectiveness of the error compensation method. The experimental results show that the average absolute positional error is reduced to 0.1150 mm from 0.7168 mm, and that the maximum absolute positional error is reduced to 0.2664 mm from 1.3073 mm. The experimental results also demonstrate that the proposed method can improve the absolute position accuracy of aviation robot and could meet the requirement of aircraft assembly.
KeywordsError similarity Spatial correlation Cross-variogram Co-kriging Error compensation Aviation drilling robot
Inverse Distance Weighted
Unable to display preview. Download preview PDF.
- 2.Atkinson, J., Hartmann, J., Jones, S., and Gleeson, P., “Robotic Drilling System for 737 Ailesron,” SAE Technical Paper, No. 2007-01-3821, 2007.Google Scholar
- 3.Zhao, J., Guo, H., Wang, L., Wang, Z., Chen, H., et al., “Study and Application Technology on Digital Flexible Accurate Assembly for Aircraft,” Aeronautical Manufacturing Technology, No. 21, pp. 32–35, 2014.Google Scholar
- 11.Stone, H. W. and Sanderson, A. C., “Statistical Performance Evaluation of the S-Model Arm Signature Identification Technique,” Proc. of IEEE International Conference on Robotics and Automation, pp. 939–946, 1988.Google Scholar
- 12.Stone, H., Sanderson, A., and Neuman, C., “Arm Signature Identification,” Proc. of IEEE International Conference on Robotics and Automation, pp. 41–48, 1986.Google Scholar
- 13.Bai, Y., Cong, M., Yang, X., and Liu, D., “Kinematic Parameter Identification for 6R Serial Robots Based on a 6-Parameter Model,” Robot, Vol. 37, No. 4, pp. 486–492, 2015.Google Scholar
- 17.Gatti, G. and Danieli, G., “A Practical Approach to Compensate for Geometric Errors in Measuring Arms: Application to a Six-Degreeof-Freedom Kinematic Structure,” Measurement Science and Technology, Vol. 19, No. 1, Paper No. 015107, 2007.Google Scholar
- 25.Nguyen, H.-N., Zhou, J., Kang, H.-J., and Ro, Y.-S., “Robot Geometric Parameter Identification with Extended Kalman Filtering Algorithm,” Proc. of International Conference on Intelligent Computing, pp. 165–170, 2013.Google Scholar
- 33.Aoyagi, S., Kohama, A., Nakata, Y., Hayano, Y., and Suzuki, M., “Improvement of Robot Accuracy by Calibrating Kinematic Model Using a Laser Tracking System-Compensation of Non-Geometric Errors Using Neural Networks and Selection of Optimal Measuring Points Using Genetic Algorithm,” Proc. of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5660–5665, 2010.CrossRefGoogle Scholar