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Modeling and assessment of the backlash error of an industrial robot

Published online by Cambridge University Press:  16 January 2012

Mohamed Slamani
Affiliation:
École de Technologie Supérieure, Montreal, QC, Canada
Albert Nubiola
Affiliation:
École de Technologie Supérieure, Montreal, QC, Canada
Ilian A. Bonev*
Affiliation:
École de Technologie Supérieure, Montreal, QC, Canada
*
*Corresponding author. E-mail: [email protected]

Summary

This paper proposes an experimental approach for evaluating the backlash error of an ABB IRB 1600 industrial serial robot under various conditions using a laser interferometer measurement instrument. The effects of the backlash error are assessed by experiments conducted on horizontal and vertical paths. A polynomial model was used to represent the relationship between the backlash error and the robot configuration. A strategy based on statistical tests was developed to choose the degree of polynomial representing the effect of the tool center point (TCP) speed and payload. Results show that the backlash error strongly affects the repeatability of the industrial robot. Statistical analyses prove that the backlash is highly dependent on both robot configuration and TCP speed, whereas it remains nearly unaffected by changes in the payload. It was discovered that the backlash error as measured at the TCP may exceeds 100 μm, and that the positive backlash error increases and the negative backlash error decreases when there is increase in TCP speed.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012

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References

1. Summers, M., “Robot capability test and development of industrial robot positioning system for the aerospace industry,” Proceedings of the SAE 2005 AeroTech Congress & Exhibition, Grapevine, TX, USA, Paper No. 2005-01-3336 (2005).Google Scholar
2. International Standardisation Organisation, “Manipulating industrial robots–performance criteria and related test methods,” ISO 9283 (ISO, Geneva, Switzerland, 1998).Google Scholar
3. Hayati, S., Tso, K. and Roston, G., “Robot Geometry Calibration,” In: Proceedings of the 1988 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA (1988), vol. 942, pp. 947951.CrossRefGoogle Scholar
4. Young, K. and Pickin, C. G., “Accuracy assessment of the modern industrial robot,” Ind. Robot: Int. J. 27 (6), 427436 (2000).CrossRefGoogle Scholar
5. Dagalakis, N. G. and Myers, D. R., “Adjustment of robot joint gear backlash using the robot joint test excitation technique,” Int. J. Robot. Res. 4 (2), 6579 (1985).CrossRefGoogle Scholar
6. Sarkar, N., Ellis, R. E. and Moore, T. N., “Backlash detection in geared mechanisms: Modeling, simulation, and experimentation,” Mech. Syst. Signal Process. 11 (3), 391408 (1997).CrossRefGoogle Scholar
7. Whitney, D. E., Lozinski, C. A. and Rourke, J. M., “Industrial robot forward calibration method and results,” J. Dyn. Syst. Meas. Control 108 (1), 18 (1986).CrossRefGoogle Scholar
8. Lima, M. F. M., Machado, J. A. T. and Crisostomo, M., “Experimental backlash study in mechanical manipulators,” Robotica 29 (2), 211219 (2011).CrossRefGoogle Scholar
9. Azenha, A. and Machado, J. A. T., “Variable structure control of robots with nonlinear friction and backlash at the joints,” In: Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA (1996) pp. 366371.CrossRefGoogle Scholar
10. Ma, C. and Hori, Y., “The application of fractional order control to backlash vibration suppression,” In: Proceedings of American Control Conference, Boston, MA, USA (2004) pp. 29012906.Google Scholar
11. Mei, Z. Q., Yang, R. Q., Liang, Ch. and Li, G. B., “The study of backlash compensation and its application in the robot checking the filter,” Int. J. Adv. Manuf. Technol. 25, 396401 (2005).CrossRefGoogle Scholar
12. Ruderman, M., Hoffmann, F. and Bertram, T., “Modeling and identification of elastic robot joints with hysteresis and backlash,” IEEE Trans. Ind. Electron. 56 (10), 38403847 (2009).CrossRefGoogle Scholar
13. International Standardisation Organisation, “Manipulating industrial robots–informative guide on test equipment and metrology methods of operation for robot performance evaluation in accordance with ISO 9283ISO TR 13309 (ISO, Geneva, Switzerland, 1995).Google Scholar
14. Slamani, M., Nubiola, A. and Bonev, I. A., “Assessment of the positioning performance of an industrial robot,” Ind. Robot: Int. J. 39 (1), (2012), http://www.emeraldinsight.com/journals.htm?articleid=1958519.CrossRefGoogle Scholar
15. Slamani, M., Mayer, J. R. R and Cloutier, G. M, “Modeling and experimental validation of machine tool motion errors using degree optimized polynomial, including motion hysteresis,” Exp. Tech. J. 35 (1), 3744 (2011).CrossRefGoogle Scholar
16. Berenson, M. L., Levine, D. M. and Goldstein, M., Intermediate Statistical Methods and Applications: A Computer Package Approach (Prentice-Hall, Englewood Cliffs, NJ, 1983).Google Scholar
17. International Standardisation OrganisationTest Code for Machine Tools – Part 2: Determination of Accuracy and Repeatability of Positioning of Numerically Controlled Axes,” 2nd ed. ISO 230–2 (ISO, Geneva, Switzerland, 1997).Google Scholar