Advertisement

Consistent kinematics and dynamics calibration of lightweight redundant industrial manipulators

  • Sergey KolyubinEmail author
  • Anton Shiriaev
  • Anthony Jubien
ORIGINAL ARTICLE
  • 31 Downloads

Abstract

Absolute accuracy is one of industrial manipulator’s key performance characteristics, which is critical for emerging robotics applications such as laser cutting, riveting, and carbon fiber placement as well as for many machining operations. On the other hand, arrival of new uses such as collaborative robots needs the estimation of interaction efforts with the operator or with the environment (hand-guiding, collision detection, and free backlash assembly). This paper presents an approach to organize an integrated kinematic and dynamic calibration procedure to improve quality of models appropriate for trajectory planning and motion control. Along with bringing theoretical insights and novel arguments, we give hands-on recommendations on selection of parameters priors, initial guesses on calibration poses and trajectories, setting active constraints, algorithms tuning, and experimental data filtering which is necessary to perform consistent robot calibration in practice. We illustrate the study with experimental data and description of actual calibration performed on the KUKA Light-Weight Robot using vision-based metrology and dedicated software. In contrast to authors preceding works, this paper includes a more complete entire procedure description, analysis of dynamic calibration sensitivity with respect to kinematic parameters estimates and a chapter on how calibration results can be used for model-based trajectories planning using virtual holonomic constraints approach.

Keywords

Industrial manipulator Robot calibration Collaborative robots Redundant kinematics Dynamics identification Optimization methods 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Supplementary material

(MP4 22.6 MB)

(MP4 81.6 MB)

170_2018_2868_MOESM3_ESM.xlsx (583 kb)
(XLSX 583 KB)

References

  1. 1.
    Aranovskiy S, Bobtsov A, Ortega R, Pyrkin A (2016) Parameters estimation via dynamic regressor extension and mixing. Proc. of the American Control Conf July, 6971–6976Google Scholar
  2. 2.
    Bargsten V, Zometa P, Findeisen R (2013) Modeling, parameter identification and model-based control of a lightweight robotic manipulator. In: 2013 IEEE international conference on control applications (CCA), pp 134–139.  https://doi.org/10.1109/CCA.2013.6662756
  3. 3.
    Bischoff R, Kurth J, Schreiber G, Koeppe R, Albu-Schaeffer A, Beyer A, Eiberger O, Haddadin S, Stemmer A, Grunwald G, Hirzinger G (2010) The kuka-dlr lightweight robot arm - a new reference platform for robotics research and manufacturing. In: Rob. (ISR), 2010 41st International Symposium on and 2010 6th German Conf. on Rob. (ROBOTIK), pp 1–8Google Scholar
  4. 4.
    Bobrow J, Dubowsky S, Gibson J (1985) Time-optimal control of robotic manipulators along specified paths. The Int J Rob Res 4(3):3–17CrossRefGoogle Scholar
  5. 5.
    Briot S, Gautier M, Jubien A (2014) In situ calibration of joint torque sensors of the kuka lightweight robot using only internal controller data. In: 2014 IEEE/ASME international conference on advanced intelligent mechatronics, pp 470–475Google Scholar
  6. 6.
    Chen Q, Chen W, Yang G, Liu R (2013) An integrated two-level self-calibration method for a cable-driven humanoid arm. IEEE Trans Autom Sci Eng 10(2):380–391.  https://doi.org/10.1109/TASE.2013.2242199 CrossRefGoogle Scholar
  7. 7.
    Gang C, Ton L, Ming C, Xuan JQ, Xu SH (2014) Review on kinematics calibration technology of serial robots. Int J Precis Eng Manuf 15(8):1759–1774CrossRefGoogle Scholar
  8. 8.
    Daney D, Papegay Y, Madeline B (2005) Choosing measurement poses for robot calibration with the local convergence method and tabu search. The Int J Rob Res 24(6):501–518CrossRefGoogle Scholar
  9. 9.
    Freidovich L, Robertsson A, Shiriaev A, Johansson R (2010) LuGre,-model-based friction compensation. IEEE Trans Control Syst Technol 18(1):194–200CrossRefGoogle Scholar
  10. 10.
    Gautier M, Khalil W (1990) Direct calculation of minimum set of inertial parameters of serial robots. IEEE Trans Robot Automat 6(3):368–373CrossRefGoogle Scholar
  11. 11.
    Gaz C, Flacco F, Luca AD (2014) Identifying the dynamic model used by the kuka lwr: a reverse engineering approach. In: 2014 IEEE Int. Conf. on Rob. and Automation (ICRA), pp 1386–1392Google Scholar
  12. 12.
    Gaz C, Flacco F, Luca AD (2016) Extracting feasible robot parameters from dynamic coefficients using nonlinear optimization methods. In: 2016 IEEE international conference on robotics and automation (ICRA), pp 2075–2081Google Scholar
  13. 13.
    Hollerbach J (1984) Dynamic scaling of manipulator trajectories. ASME J Dyn Syst Measurement, and Control 106(1):102–106CrossRefGoogle Scholar
  14. 14.
    Hollerbach J, Khalil W, Gautier M (2008) Model identification. In: Siciliano B, Khatib O (eds) Springer Handbook of Rob. Springer, Berlin, pp 321–344CrossRefGoogle Scholar
  15. 15.
    Hollerbach JM, Wampler CW (1996) The calibration index and taxonomy for robot kinematic calibration methods. The Int J Rob Res 15(6):573–591CrossRefGoogle Scholar
  16. 16.
    Janot A, Vandanjon PO, Gautier M (2014) A generic instrumental variable approach for industrial robot identification. IEEE Trans Control Syst Technol 22(1):132–145CrossRefGoogle Scholar
  17. 17.
    Jubien A, Gautier M, Janot A (2014) Dynamic identification of the Kuka LWR robot using motor torques and joint torque sensors data. IFAC Proc 19:8391–8396CrossRefGoogle Scholar
  18. 18.
    Khalil W, Besnard S, Lemoine P (2000) Comparison study of the geometric parameters calibration methods. Int J Rob Auto 15(2):56–67Google Scholar
  19. 19.
    Khalil W, Creusot D (1997) SYMORO+: a system for the symbolic modelling of robots. Robotica 15 (2):153–161CrossRefGoogle Scholar
  20. 20.
    Khalil W, Dombre E (2004) Modeling, identification and control of robots. Elsevier ScienceGoogle Scholar
  21. 21.
    Khalil W, Gautier M (1991) Calculation of the identifiable parameters for robot calibration. In: The 9th IFAC/IFORS symposium on identification and system parameter estimation, pp 888–892. Budapest, HungaryCrossRefGoogle Scholar
  22. 22.
    Khalil W, Gautier M, Enguehard C (1991) Identifiable parameters and optimum configurations for robots calibration. Robotica 9(01):63–70CrossRefGoogle Scholar
  23. 23.
    Khalil W, Kleinfinger J (1986) A new geometric notation for open and closed-loop robots. In: Proceedings of 1986 IEEE Int. Conf. on Rob. and Automation, vol 3, pp 1174–1179Google Scholar
  24. 24.
    Khalil W, Lemoine P (1999) GECARO: a system for the geometric calibration of robots. APII-JESA European J Automation 33(5-6):717–739Google Scholar
  25. 25.
    Klodmann J, Lakatos D, Ott C, Albu-Schäffer A (2015) A closed-form approach to determine the base inertial parameters of complex structured robotic systems. IFAC-PapersOnLine 48(1):316–321CrossRefGoogle Scholar
  26. 26.
    Kolyubin S, Paramonov L, Shiriaev A (2015) Robot kinematics identification: KUKA LWR4+ redundant manipulator example. J Phys Conf Ser 659(1):012,011CrossRefGoogle Scholar
  27. 27.
    Kolyubin S, Shiriaev A, Jubien A (2017) Refining dynamics identification for co-bots: Case study on KUKA LWR4+. In: Preprints of the 20th IFAC World Congress, pp 15,191–15, 196Google Scholar
  28. 28.
    Kolyubin SA, Paramonov L, Shiriaev AS (2015) Optimising configurations of KUKA LWR4+ manipulator for calibration with optical cmm. In: Bai S, Ceccarelli M (eds) Recent advances in mechanism design for rob., mechanisms and machine science, vol 33. Springer Int. Publishing, pp 189–199Google Scholar
  29. 29.
    Lehmann C, Olofsson B, Nilsson K, Halbauer M, Haage M, Robertsson A, Sȯrnmo O, Berger U (2013) Robot joint modeling and parameter identification using the clamping method. IFAC Proc pp 813–818CrossRefGoogle Scholar
  30. 30.
    Marie S, Courteille E, Maurine P (2013) Elasto-geometrical modeling and calibration of robot manipulators: application to machining and forming applications. Mech Mach Theory 69:13–43CrossRefGoogle Scholar
  31. 31.
    Nubiola A, Bonev IA (2013) Absolute calibration of an abb IRb 1600 robot using a laser tracker. Rob Comput Integr Manuf 29(1):236–245CrossRefGoogle Scholar
  32. 32.
    Pchelkin S, Shiriaev A, Robertsson A, Freidovich L, Kolyubin S, Paramonov L, Gusev S (2017) On orbital stabilization for industrial manipulators: case study in evaluating performances of modified PD+ and inverse dynamics controllers. IEEE Trans Control Syst Technol 25(1):101–117CrossRefGoogle Scholar
  33. 33.
    Rackl W, Lampariello R, Hirzinger G (2012) Robot excitation trajectories for dynamic parameter estimation using optimized b-splines. In: 2012 IEEE international conference on robotics and automation, pp 2042–2047Google Scholar
  34. 34.
    Renaud P, Andreff N, Lavest JM, Dhome M (2006) Simplifying the kinematic calibration of parallel mechanisms using vision-based metrology. IEEE Trans Robot 22(1):12–22CrossRefGoogle Scholar
  35. 35.
    Shiriaev A, Freidovich L, Gusev S (2010) Transverse linearization for controlled mechanical systems with several passive degrees of freedom. IEEE Trans Autom Control 55(4):893–906MathSciNetCrossRefGoogle Scholar
  36. 36.
    Stürz YR, Affolter LM, Smith RS (2017) Parameter identification of the kuka lbr iiwa robot including constraints on physical feasibility. IFAC-PapersOnLine 50(1):6863–6868CrossRefGoogle Scholar
  37. 37.
    Swevers J, Ganseman C, Tukel D, de Schutter J, Brussel HV (1997) Optimal robot excitation and identification. IEEE Trans Robot Automat 13(5):730–740.  https://doi.org/10.1109/70.631234 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Technologies and ControlITMO UniversitySt. PetersburgRussia
  2. 2.Department of Engineering CyberneticsNTNUTrondheimNorway
  3. 3.Nantes Digital Sciences Laboratory – LS2NNantesFrance

Personalised recommendations