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Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task

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Abstract

Robot geometrical calibration aims at reducing the global positioning accuracy of a robotic arm by correcting the theoretical values of the kinematic parameters. A novel method for the geometrical calibration of robotic arms used in industrial applications is proposed. The proposed approach mainly focuses on the final positional accuracy of the robotic tool center point (TCP) when executing an industrial task rather than on the accurate estimation of the kinematic parameters themselves, as done so far by many calibration methods widely discussed in literature. A real industrial use-case is presented, and the steps of the proposed calibration procedure for the robotic arm are described. Experimental methodology and results for the identification of geometrical parameters are also discussed. A practical validation of the final positional accuracy of the robotic arm (after kinematic calibration) was performed, and experimental results validated the proposed procedure, proving its feasibility and effectiveness in the considered industrial scenario.

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References

  1. Cholewa A, Świder J, Zbilski A (2016) Verification of forward kinematics of the numerical and analytical model of Fanuc AM100iB robot. IOP Conf. Ser.: Mater Sci. Eng 145(1):052001

    Article  Google Scholar 

  2. Conrad KL, Shiakolas PS, Yih TC (2000) Robotic calibration issues: accuracy, repeatability and calibration. In: Proceedings of the 8th mediterranean conference on control & automation

  3. Kluz R, Trzepieciński T (2014) The repeatability positioning analysis of the industrial robot arm. Assembly 34(3):285–295

    Article  Google Scholar 

  4. Motta J, Carvalho GC, McMaster RS (2001) Robot calibration using a 3D vision-based measurement system with a single camera. Robotics and Computer Integrated Manufacturing 17(1):487–497

    Article  Google Scholar 

  5. Du G, Zhang P (2013) Online robot calibration based on vision measurement. Robot. Comput. Integr. Manuf. 29(1):484–492

    Article  Google Scholar 

  6. Shirinzadeh B, Teoh PL, Tian Y, Dalvand MM, Zhong Y, Liaw HC (2010) Laser interferometry-based guidance methodology for high precision positioning of mechanisms and robots. Robot. Comput. Integr. Manuf. 26(1):74–82

    Article  Google Scholar 

  7. He S, Ma L, Yan C, et al. (2019) Multiple location constraints based industrial robot kinematic parameter calibration and accuracy assessment. Int J Adv Manuf Technol 102:1037–1050

    Article  Google Scholar 

  8. Mao CT, Li S, Chen ZW et al (2019) A novel algorithm for robust calibration of kinematic manipulators and its experimental validation. IEEE Access 7:90487–90496

    Article  Google Scholar 

  9. Du GL, Zhang P (2014) Online serial manipulator calibration based on multisensory process via extended Kalman and particle filters. IEEE Trans. Ind. Electron. 61(12):6852–6859

    Article  Google Scholar 

  10. Li Z, Li S, Luo X (2020) An overview of calibration technology of industrial robots. IEEE/CAA J. Autom. Sinica, p 7

  11. Summers M (2005) Robot capability test and development of industrial robot positioning system for the aerospace industry. SAE Trans. 114:1108–1118

    Google Scholar 

  12. Nubiola A, Bonev IA (2014) Absolute robot calibration with a single telescoping ballbar. Precis. Eng. 38(1):472–480

    Article  Google Scholar 

  13. Lehmann C, Pellicciari M, Drust M, Gunnink JW (2013) Machining with industrial robots: the COMET project approach. In: International Workshop on Robotics in Smart Manufacturing: 27–36

  14. Zeng Y, Tian W, Li D et al (2017) An error-similarity-based robot positional accuracy improvement method for a robotic drilling and riveting system. Int J Adv Manuf Technol 88:2745–2755

    Article  Google Scholar 

  15. Leali F, Vergnano A, Pini F, et al. (2016) A workcell calibration method for enhancing accuracy in robot machining of aerospace parts. Int J Adv Manuf Technol 85:47–55

    Article  Google Scholar 

  16. Janez G, Timi K, Karl G et al (2020) Accuracy improvement of robotic machining based on robot’s structural properties. Int J Adv Manuf Technol 108:1309–1329

    Article  Google Scholar 

  17. ISO Technical Committee 299 Robotics (1998) ISO 9283:1998(R2015) Manipulating industrial robots – performance criteria and related test methods

  18. American National Standards Institute (1990) ANSI/RIA R15.05-1-1990 (R1999) American national standard for industrial robots and robot systems - point-to-point and static performance characteristics – Evaluation

  19. Schneider U, Drust M, Ansaloni M, Lehmann C, Pellicciari M, Leali F, Gunnink JW, Verl A (2016) Improving robotic machining accuracy through experimental error investigation and modular compensation. Int J Adv Manuf Technol 85:3–15

    Article  Google Scholar 

  20. Mustafa SK, Pey YT, Yang G, Chen I (2010) A geometrical approach for online error compensation of industrial manipulator. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics: 738–743

  21. Elatta A, Gen LP, Zhi FL, Daoyuan Y, Fei L (2004) An overview of robot calibration. Inform. Technol. J. 3:74–78

    Article  Google Scholar 

  22. Płaczek M, Piszczek L (2018) Testing of an industrial robot’s accuracy and repeatability in off and online environment, vol 20, pp 455–464

  23. Cristalli C, Boria S, Massa D, Lattanzi L, Concettoni E (2016) Smart robotic cell: a cyber-physical system approach for the factory of the future. In: In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

  24. Cristalli C, Lattanzi L, Massa D, Angione G (2017) Cognitive robot referencing system for high accuracy manufacturing task. Procedia Manufacturing 11:405–412

    Article  Google Scholar 

  25. Hollerbach JJ (1989) A review of kinematic calibration. In: Khatib O, Craig JJ, Lozanol-Perez T (eds) The robotics review, vol 1. MIT Press, Cambridge, pp 207–242

  26. Wu Y, Klimchik A, Caro S, Furet B, Pashkevich A (2015) Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments. Robot. Comput. Integr. Manuf. 35:151–168

    Article  Google Scholar 

  27. Santolaria J, Ginés M (2013) Uncertainty estimation in robot kinematic calibration. Robot. Comput. Integr. Manuf. 29(2):370–384

    Article  Google Scholar 

  28. Nicholas JH (1986) Computing the polar decomposition with applications. J. Sci. Comput 7:1160–1174

    MathSciNet  MATH  Google Scholar 

  29. Borm JH, Meng CH (1991) Determination of optimal measurement configurations for robot calibration based on observability measure. Int. J. Robot. Res. 10:51–63

    Article  Google Scholar 

  30. Driels MR, Pathre US (1990) Significance of observation strategy on the design of robot calibration experiments. J. Robot. Syst. 7:197–223

    Article  Google Scholar 

  31. Craig JJ (1986) Introduction to robotics: mechanics and control, 3rd edn. Addison-Wesley, Boston

    Google Scholar 

  32. Daney D, Papegay Y, Madeline B (2005) Choosing measurement poses for robot calibration with the local convergence method and Tabu search. Int. J. Robot. Res. 24:501–518

    Article  Google Scholar 

  33. Sun Y, Hollerbach JM (2008) Observability index selection for robot calibration. In: Proceedings of the IEEE International Conference on Robotics and Automation ICRA: 831–836

  34. National Instruments (2020) National Instruments Linux kernel release. https://github.com/ni/linux. Accessed 25 July 2020

  35. PAL Robotics (2020) TIAGo robot. http://pal-robotics.com/robots/tiago/. Accessed 13 September 2020

  36. Faro (2020) Vantage laser tracker. https://www.faro.com/products/3d-manufacturing/faro-laser-tracker/. Accessed 25 July 2020

  37. Hage H, Bidaud P, Jardin N (2011) Practical consideration on the identification of the kinematic parameters of the Stäubli TX90 robot. In: Proceedings of the 13th World Congress in Mechanism and Machine Science: 43

  38. Agarwal S, Mierle K et al (2018) Ceres Solver. http://ceres-solver.org. Accessed 25 July 2020

  39. 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

    Article  Google Scholar 

  40. Pukelsheim F (1994) The three sigma rule. Am. Stat. 48(2):88–91

    MathSciNet  Google Scholar 

  41. Wooluru Y, Swamy DR, Nagesh P (2014) The process capability analysis - a tool for process performance measures and metrics - a case study. Int. J. Qual. Res 806(3):399–416

    Google Scholar 

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Acknowledgements

The authors would like to thank their colleagues Dr. Marc Douilly (from STELIA Aerospace) who provided insight and useful suggestions that greatly helped, and Dr. Olivier Stasse (from CNRS-LAAS) and His Team for the assistance during the experiments and validation phase on the TIAGo robot.

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Authors and Affiliations

  1. Università degli Studi di Modena e Reggio Emilia, Via Amendola 2, 42122, Reggio Emilia, Italy

    Luca Lattanzi & Marcello Pellicciari

  2. Loccioni Group, Via Fiume 16, 60030, Angeli di Rosora, Italy

    Cristina Cristalli & Daniele Massa

  3. Airbus SAS, 1 Rond Point Maurice Bellonte, 31700, Blagnac, France

    Sébastien Boria & Pierre Lépine

Authors
  1. Luca Lattanzi
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  2. Cristina Cristalli
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  3. Daniele Massa
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  4. Sébastien Boria
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  5. Pierre Lépine
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  6. Marcello Pellicciari
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Correspondence to Luca Lattanzi.

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Lattanzi, L., Cristalli, C., Massa, D. et al. Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task. Int J Adv Manuf Technol 111, 1813–1829 (2020). https://doi.org/10.1007/s00170-020-06179-9

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  • DOI: https://doi.org/10.1007/s00170-020-06179-9

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