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Novel kinematic model of a SCARA-type robot with bi-directional angular positioning deviation of rotary axes

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Abstract

This paper proposed a kinematic model and its calibration scheme to further improve an industrial robots absolute positioning accuracy over the entire workspace. To demonstrate the proposed model and its effectiveness in simplified kinematics, this paper only targets a SCARA (Selective Compliance Assembly Robot Arm)-type robot. The proposed model includes not only link length errors and rotary axis angular offsets, widely known as the Denavit-Hartenberg (D-H) parameters, but also the “error map” of the angular positioning deviation of each rotary axis, modelled as a function of command angular position, and the rotation direction to model the influence of backlash. The angular positioning deviation of each rotary axis is identified by measuring the robots end-effector position by a laser tracker with indexing each rotary axis at prescribed angular positions. To verify the validity of the identified model, the effectiveness of the compensation based on it is experimentally investigated. By the compensation, the robot’s average absolute position error was reduced by 33% to 0.034mm. Furthermore, this paper experimentally demonstrates that the proposed model can be extended to the radial error motion, axis-to-axis cross talk, and the three-dimensional positioning with orientation errors of axis average lines.

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The data and material that support the findings of this study are available on request.

References

  1. Lee HM, Kim JB (2013) A survey on robot teaching: categorization and brief review. Appl Mech Mater 330:648–656, https://www.scientific.net/AMM.330.648

  2. ISO 230-1:2012, Test code for machine tools — part 1: geometric accuracy of machines operating under no-load or quasi-static conditions

  3. Verl A, Valente A, Melkote S et al (2019) Robots in machining. CIRP Ann 68:799–822. https://doi.org/10.1016/j.cirp.2019.05.009

    Article  Google Scholar 

  4. Chen Y, Dong F (2013) Robot machining: Recent development and future research issues. Int J Adv Manuf Technol 66:1489–1497. https://doi.org/10.1007/s00170-012-4433-4

    Article  Google Scholar 

  5. Uriarte L, Zatarain M, Axinte D et al (2013) Machine tools for large parts. CIRP Ann Manuf Technol 62:731–750. https://doi.org/10.1016/j.cirp.2013.05.009

    Article  Google Scholar 

  6. Bhatt PM, Malhan RK, Shembekar AV et al (2020) Expanding capabilities of additive manufacturing through use of robotics technologies: a survey. https://doi.org/10.1016/j.addma.2019.100933

  7. Lakhal O, Chettibi T, Belarouci A et al (2020) Robotized additive manufacturing of funicular architectural geometries based on building materials. IEEE/ASME Trans Mechatron 25:2387–2397. https://doi.org/10.1109/TMECH.2020.2974057

    Article  Google Scholar 

  8. Chromy A (2015) High-accuracy volumetric measurements of soft tissues using robotic 3D scanner. IFAC-PapersOnLine 28:318–323. https://doi.org/10.1016/j.ifacol.2015.07.054

    Article  Google Scholar 

  9. ISO 230-7: 2015, Test code for machine tools — part 7: geometric accuracy of axes of rotation

  10. Meggiolaro MA, Scriffignano G, Dubowsky S (2000) Manipulator calibration using a single endpoint contact constraint. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 35173:759–767. https://doi.org/10.1115/DETC2000/MECH-14129

    Article  Google Scholar 

  11. Bettahar H, Lehmann O, Clvy C et al (2020) Photo-robotic extrinsic parameters calibration of 6-DOF robot for high positioning accuracy. IEEE/ASME Trans Mechatron 25:616–626. https://doi.org/10.1109/TMECH.2020.2965255

    Article  Google Scholar 

  12. Chen H, Fuhlbrigge T, Choi S et al (2008) Practical industrial robot zero offset calibration. IEEE Int Conf Auto Sci Eng 2008:516–521. https://doi.org/10.1109/COASE.2008.4626417

    Article  Google Scholar 

  13. Liu Y, Shi D, Ding J (2014) An automated method to calibrate industrial robot kinematic parameters using Spherical Surface constraint approach. The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent 2014:365-370. https://doi.org/10.1109/CYBER.2014.6917491

  14. Joubair A, Bonev IA (2015) Kinematic calibration of a six-axis serial robot using distance and sphere constraints. Int J Adv Manuf Technol 77:515–523. https://doi.org/10.1007/s00170-014-6448-5

    Article  Google Scholar 

  15. Cai Y, Gu H, Li C et al (2017) Easy industrial robot cell coordinates calibration with touch panel. Robot Comput-Int Manuf 50:276–285. https://doi.org/10.1016/j.rcim.2017.10.004

    Article  Google Scholar 

  16. Nubiola A, Bonev IA (2013) Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot Comput-Int Manuf 29:236–245. https://doi.org/10.1016/j.rcim.2012.06.004

    Article  Google Scholar 

  17. Nubiola A, Slamani M, Joubair A et al (2014) Comparison of two calibration methods for a small industrial robot based on an optical CMM and a laser tracker. Robotica 32:447–466. https://doi.org/10.1017/S0263574713000714

    Article  Google Scholar 

  18. Švaco M, Šekoranja B, Šuligoj F et al (2014) Calibration of an industrial robot using a stereo vision system. Procedia Engineering 69:459–463. https://doi.org/10.1016/j.proeng.2014.03.012

    Article  Google Scholar 

  19. Zhang X, Song Y, Yang Y et al (2017) Stereo vision based autonomous robot calibration. Robot Auton Syst 93:43–51. https://doi.org/10.1016/j.robot.2017.04.001

    Article  Google Scholar 

  20. Filion A, Joubair A, Tahan AS et al (2018) Robot calibration using a portable photogrammetry system. Robot Comput-Int Manuf 49:1339–1351. https://doi.org/10.1016/j.rcim.2017.05.004

    Article  Google Scholar 

  21. Hong C, Ibaraki S, Matsubara A (2011) Influence of position-dependent geometric errors of rotary axes on a machining test of cone frustum by five-axis machine tools. Precis Eng 35:1–11. https://doi.org/10.1016/j.precisioneng.2010.09.004

    Article  Google Scholar 

  22. Lee K, Choi J, Bang Y (2016) Shaft position measurement using dual absolute encoders. Sens Actuators, A 238:276–281. https://doi.org/10.1016/j.sna.2015.12.027

    Article  Google Scholar 

  23. Wu Y, Klimchik A, Caro S et al (2015) Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments. Robot Comput-Int Manuf 35:151–168. https://doi.org/10.1016/j.rcim.2015.03.007

    Article  Google Scholar 

  24. Ibaraki S, Theissen NA, Archenti A (2021) Evaluation of kinematic and compliance calibration of serial articulated industrial manipulators. Int J Auto Tech 15:567–580. https://doi.org/10.20965/ijat.2021.p0567

    Article  Google Scholar 

  25. Zhuang H, Wu J, Huang W (1997) Optimal planning of robot calibration experiments by genetic algorithms. J Robot Syst 14:741–752. https://doi.org/10.1002/(SICI)1097-4563(199710)14:10/741::AID-ROB4/3.0.CO;2-U

    Article  Google Scholar 

  26. Zhao G, Zhang P, Ma G et al (2019) System identification of the nonlinear residual errors of an industrial robot using massive measurements. Robot Comput-Int Manuf 59:104–114. https://doi.org/10.1016/j.rcim.2019.03.007

    Article  Google Scholar 

  27. Chen D, Li S, Liao L (2019) A recurrent neural network applied to optimal motion control of mobile robots with physical constraints. Appl Soft Comput 85:105880. https://doi.org/10.1016/j.asoc.2019.105880

  28. Slamani M, Nubiola A, Bonev IA (2012) Modeling and assessment of the backlash error of an industrial robot. Robotica 30:1167–1175. https://doi.org/10.1017/S0263574711001287

    Article  Google Scholar 

  29. Ruderman M, Hoffmann F, Bertram T (2009) Modeling and identification of elastic robot joints with hysteresis and backlash. IEEE Trans Industr Electron 56:3840–3847. https://doi.org/10.1109/TIE.2009.2015752

    Article  Google Scholar 

  30. Hörler P, Kronig L, Kneib JP et al (2018) High density fiber postitioner system for massive spectroscopic surveys. Mon Not R Astron Soc 481:3070–3082. https://doi.org/10.1093/mnras/sty2442

    Article  Google Scholar 

  31. Ayala HVH, dos Santos Coelho L (2012) Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator. Expert Syst Appl 39:8968–8974. https://doi.org/10.1016/j.eswa.2012.02.027

    Article  Google Scholar 

  32. Pierezan J, Freire RZ, Weihmann L et al (2017) Static force capability optimization of humanoids robots based on modified self-adaptive differential evolution. Comput Oper Res 84:205–215. https://doi.org/10.1016/j.cor.2016.10.011

    Article  MathSciNet  MATH  Google Scholar 

  33. Mesmer P, Neubauer M, Lechler A et al (2022) Robust design of independent joint control of industrial robots with secondary encoders. Robot Comput-Int Manuf 73:102232. https://doi.org/10.1016/j.rcim.2021.102232

  34. Cvitanic T, Melkote SN (2022) A new method for closed-loop stability prediction in industrial robots. Robot Comput-Int Manuf 73:102218. https://doi.org/10.1016/j.rcim.2021.102218

  35. Usui R, Ibaraki S (2022) A novel error mapping of bi-directional angular positioning deviation of rotary axes in a SCARA-robot by open-loop tracking interferometer measurement. Precision Engineering 74:60–68. https://doi.org/10.1016/j.precisioneng.2021.11.002

    Article  Google Scholar 

  36. Kawano K, Ibaraki S (2020) Estimation of thermal influence on 2D positioning error of a SCARA-type robot over the entire workspace. The 18th International Conference on Precision Engineering:C-3-2

  37. Denavit J, Hartenberg RS (1955) A kinematic notation for lower-pair mechanisms based on matrices

  38. Alam MM, Ibaraki S, Fukuda K (2021) Kinematic modeling of six-axis industrial robot and its parameter identification: a tutorial. Int J Auto Tech 15:599–610. https://doi.org/10.20965/ijat.2021.p0599

  39. Ibaraki S, Oyama C, Otsubo H (2011) Construction of an error map of rotary axes on a five-axis machining center by static R-test. Int J Mach Tools Manuf 51:190–200. https://doi.org/10.1016/j.ijmachtools.2010.11.011

    Article  Google Scholar 

  40. Cappa S, Reynaerts D, Al-Bender F (2014) Reducing the radial error motion of an aerostatic journal bearing to a nanometre level: theoretical modelling. Tribol Lett 53: 27-41.https://doi.org/10.1007/s11249-013-0241-8

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Funding

The research leading to these results received funding from Japan Society for the Promotion of Science (JSPS) KAKENHI under Grant Number JP18K03874.

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Correspondence to Soichi Ibaraki.

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Zhao, N., Ibaraki, S. Novel kinematic model of a SCARA-type robot with bi-directional angular positioning deviation of rotary axes. Int J Adv Manuf Technol 120, 4901–4915 (2022). https://doi.org/10.1007/s00170-022-08943-5

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