Industrial robots are widely used in various areas owing to their greater degrees of freedom (DOFs) and larger operation space compared with traditional frame movement systems involving sliding and rotational stages. However, the geometrical transfer of joint kinematic errors and the relatively weak rigidity of industrial robots compared with frame movement systems decrease their absolute kinematic accuracy, thereby limiting their further application in ultraprecision manufacturing. This imposes a stringent requirement for improving the absolute kinematic accuracy of industrial robots in terms of the position and orientation of the robot arm end. Current measurement and compensation methods for industrial robots either require expensive measuring systems, producing positioning or orientation errors, or offer low measurement accuracy. Herein, a kinematic calibration method for an industrial robot using an artifact with a hybrid spherical and ellipsoid surface is proposed. A system with submicrometric precision for measuring the position and orientation of the robot arm end is developed using laser displacement sensors. Subsequently, a novel kinematic error compensating method involving both a residual learning algorithm and a neural network is proposed to compensate for nonlinear errors. A six-layer recurrent neural network (RNN) is designed to compensate for the kinematic nonlinear errors of a six-DOF industrial robot. The results validate the feasibility of the proposed method for measuring the kinematic errors of industrial robots, and the compensation method based on the RNN improves the accuracy via parameter fitting. Experimental studies show that the measuring system and compensation method can reduce motion errors by more than 30%. The present study provides a feasible and economic approach for measuring and improving the motion accuracy of an industrial robot at the submicrometric measurement level.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00400-6
Ling-Bao Kong
,
Yi Yu
. Precision measurement and compensation of kinematic errors for industrial robots using artifact and machine learning[J]. Advances in Manufacturing, 2022
, 10(3)
: 397
-410
.
DOI: 10.1007/s40436-022-00400-6
1. Besset P, Olabi A, Gibaru O (2016) Advanced calibration applied to a collaborative robot. In:2016 IEEE international power electronics and motion control conference (PEMC), Varna, pp 662-667
2. Denavit J, Hartenberg RS (1955) A kinematic notation for lowerpair mechanisms based on matrix. J Appl Mech 22:215-221
3. Hayati S, Mirmirani M (1985) Improving the absolute positioning accuracy of robot manipulators. J Robotic Syst 2(4):397-413
4. Joubair A, Bonev IA (2015) Kinematic calibration of a six-axis serial robot using distance and sphere constraints. Int J Adv Manuf Technol 77(1/4):515-523
5. Stone H, Sanderson A, Neuman C (1986) Arm signature identification. In:Proceedings of IEEE international conference on robotics and automation, 7-10 April, San Francisco, pp 41-48. https://doi.org/10.1109/ROBOT.1986.1087664
6. Zhuang H, Roth ZS, Hamano F (1992) A complete and parametrically continuous kinematic model for robot manipulators. IEEE Trans Robot Autom 8(4):451-463
7. Chen IM, Yang GL, Tan CT et al (2001) Local POE model for robot kinematic calibration. Mech Mach Theory 36(11/12):1215-1239
8. Yang X, Wu L, Li J et al (2014) A minimal kinematic model for serial robot calibration using POE formula. Robot Comput Integr Manuf 30(3):326-334
9. Nubiola A, Bonev IA (2014) Absolute robot calibration with a single telescoping ballbar. Precis Eng 38(3):472-480
10. Marwan A, Simic M, Imad F (2017) Calibration method for articulated industrial robots. Procedia Comput Sci 112:1601-1610
11. Nubiola A, Bonev IA (2013) Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot Comput Integr Manuf 29(1):236-245
12. Du GL, Zhang P, Li D (2015) Online robot calibration based on hybrid sensors using Kalman filters. Robot Comput Integr Manuf 31:91-100
13. Nissler C, Marton ZC (2017) Robot-to-camera calibration:a generic approach using 6D detections. In:The 1st IEEE international conference on robotic computing (IRC), 10-12 April, Taichung, pp 299-302. https://doi.org/10.1109/IRC.2017.66
14. Joubair A, Zhao LF, Bigras P et al (2015) Absolute accuracy analysis and improvement of a hybrid 6-DOF medical robot. Industrial Robot 42(1):44-53. https://doi.org/10.1108/IR-09-2014-0396
15. Shibuya Y, Maru N (2009) Control of 6 DOF arm of the humanoid robot by linear visual servoing. In:2009 IEEE international symposium on industrial electronics, Seoul, pp 1791-1796. https://doi.org/10.1109/ISIE.2009.5218128
16. Ma G, Wang F, Qu Z et al (2006) A feasible vision-based measurement method for robot orientation error. In:The 1st international symposium on systems and control in aerospace and astronautics, Harbin, 19-21 January, pp 1214-1217
17. Motta JMST, McMaster RS (2002) Experimental validation of a 3-D vision-based measurement system applied to robot calibration. J Braz Soc Mech Sci 24(3). https://doi.org/10.1590/S0100-73862002000300011
18. Miseikis J, Glette K, Elle OJ et al (2016) Automatic calibration of a robot manipulator and multi 3D camera system. In:IEEE/SICE international symposium on system integration (SII), Sapporo, pp 735-741. https://doi.org/10.1109/SII.2016.7844087
19. Oriolo G, Paolillo A, Rosa L et al (2016) Humanoid odometric localization integrating kinematic, inertial and visual information. Auton Robots 40(5):867-879
20. Monica T, Giovanni L, PierLuigi M et al (2003) A closed-loop neuro-parametric methodology for the calibration of a 5 DOF measuring robot. In:Proceedings of IEEE international symposium on computational intelligence in robotics and automation, pp 1482-1487. https://doi.org/10.1109/CIRA.2003.1222216
21. Liu J, Zhang Y, Li Z (2007) Improving the Positioning Accuracy of a neurosurgical robot system. IEEE/ASME Trans Mechatron 12(5):527-533. https://doi.org/10.1109/TMECH.2007.905694
22. Aoyagi S, Kohama A, Nakata Y et al (2010) 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 usinggenetic algorithm. In:IEEE/RSJ international conference on intelligent robots and systems, 18-22 October, Taipei, pp 5660-5665. https://doi.org/10.1109/IROS.2010.5652953
23. Messay T, Chen C, Ordoñez R et al (2011) GPGPU acceleration of a novel calibration method for industrial robots. In:Proceedings of the IEEE national aerospace and electronics conference (NAECON), 20-22 July, Dayton, pp 124-129
24. Nguyen HN, Zhou J, Kang HJ (2015) A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network. Neurocomputing 151(3):996-1005
25. Yuan P, Chen D, Wang T et al (2018) A compensation method based on extreme learning machine to enhance absolute position accuracy for aviation drilling robot. Adv Mech Eng 10(3):1687814018763411. https://doi.org/10.1177/1687814018763411
26. Chen DD, Wang TM, Yuan PJ et al (2019) A positional error compensation method for industrial robots combining error similarity and radial basis function neural network. Meas Sci Technol 30(12):125010
27. Nguyen HN, Zhou J, Kang HJ (2015) A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network. Neurocomputing 151:996-1005
28. Leica (2021) Laser tracker systems. https://leica-geosystems.com/products/laser-tracker-systems. Accessed 8 Dec 2021
29. Faro (2021) Vantage laser trackers. https://www.faro.com/zh-CN/Products/Hardware/Vantage-Laser-Trackers. Accessed 8 Dec 2021
30. API (2021) Radian laser trackers. https://apimetrology.com/radian/. Accessed 8 Dec 2021
31. Luo Z, Sturm J (2000) Error analysis. Handb Semidefinite Program 27:163-189. https://doi.org/10.1007/978-1-4615-4381-7_7
32. Tang HY, He ZY, Ma YX et al (2017) A step identification method for kinematic calibration of a 6-DOF serial robot. Mech Mach Sci 408:1009-1020
33. He KM, Zhang XY, Ren SQ et al (2016) Deep residual learning for image recognition. In:IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, pp 770-778. https://doi.org/10.1109/CVPR.2016.90
34. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436-444
35. Huang G, Liu Z, Maaten LV et al (2016) Densely connected convolutional networks. In:Proceeding of the conference on computer vision and pattern recognition (CVPR), 21-26 July, Honolulu, pp 4700-4708
