A Framework of In Situ Model Error Compensation for Adaptive Robotic Task Execution

Article Preview

Abstract:

In precision robotic applications, inaccuracy in workpiece geometry has been a common problem to the precise processing of the workpiece. Due to manufacturing defects and workpiece deformation, the actual geometry of the workpiece deviates from its nominal 3D CAD model which is defined as model error. For many of the existing industrial robotic applications today, the robot path for processing the workpiece is planned based on the nominal 3D CAD model of the workpiece. Hence, the model error of the workpiece leads to error in the robot path planning eventually inducing inaccurate processing. To enhance the accuracy of the robot in processing the workpiece, a framework for in-situ model error compensation has been proposed. Prior to the processing of each workpiece, the proposed technique employs 3D optical laser scanning technology to capture the actual 3D model of the workpiece and compares it with the nominal model to establish the model errors. The nominal path of the robot initially created based on the nominal CAD model is then modified according to the model error. Thus, this step performs the in-situ model error compensation making the robotic task adaptive to the actual workpiece geometry. Experiments have been conducted to verify the proposed framework and an accuracy of up to 30 micrometers has been achieved in model error identification and compensation. The proposed technique can be employed in applications such as high precision robotic tasks, where accuracy of task execution is an important factor.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

675-680

Citation:

Online since:

September 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] M. Sortino, S. Belfio, B. Motyl, and G. Totis: Compensation of geometrical errors of CAM/CNC machined parts by means of 3D workpiece model adaptation, Computer-Aided Design, vol. 48 (2014), pp.28-38.

DOI: 10.1016/j.cad.2013.10.010

Google Scholar

[2] M. Oitzman, and J. Campbell: High accuracy positioning system implements robotic applications with guaranteed placement accuracy, The Industrial Robot, vol. 27, no. 4 (2000), pp.274-278.

DOI: 10.1108/01439910010372091

Google Scholar

[3] E. Abele, K. Schützer, J. Bauer, and M. Pischan: Tool path adaption based on optical measurement data for milling with industrial robots, Production Engineering, vol. 6, no. 4-5 (2012), pp.459-465.

DOI: 10.1007/s11740-012-0383-9

Google Scholar

[4] S. J. Hong: Method for correcting teaching points for welding robot and welding robot system employing the same, U.S. Patent 6452134 B2 (2002).

Google Scholar

[5] N. Jayaweera, and P. Webb: Robotic edge profiling of complex components, Industrial Robot: An International Journal, vol. 38, no. 1 (2011), pp.38-47.

DOI: 10.1108/01439911111097832

Google Scholar

[6] P. C. Tung, and S. C. Chen: Trajectory planning for automated robotic deburring on an unknown contour, International Journal of Machine Tools & Manufacture, vol. 40, no. 2000 (1999), p.957–978.

DOI: 10.1016/s0890-6955(99)00099-1

Google Scholar

[7] A. Reek: Strategies to focus location in laser welding, Herbert Utz Verlag, Germany (2000).

Google Scholar

[8] B. Regaard, S. Kaierle, and R. Poprawe: Seam-tracking for high precision laser welding applications—Methods, restrictions and enhanced concepts, Journal of Laser Applications, vol. 21, no. 4 (2009), pp.183-195.

DOI: 10.2351/1.3267476

Google Scholar

[9] A. Brière-Côté, L. Rivest, and R. Maranzana: 3D CAD Model Comparison: An Evaluation of Model Difference Identification Technologies, Computer-Aided Design and Applications, vol. 10, no. 2 (2013), pp.173-195.

DOI: 10.3722/cadaps.2013.173-195

Google Scholar

[10] M. Schultz: Production quality for 3D laser beam welding of sheet metal components, University Erlangen-Nuernberg, Germany (1997), p.58.

Google Scholar