Inverse dynamic analysis and position error evaluation of the heavy-duty industrial robot with elastic joints: an efficient approach based on Lie group

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Abstract

Heavy-duty industrial robots have great advantages in the manufacturing industry. Considering the heavy process load and low stiffness of the robot, an accurate and efficient dynamic model plays an important role in the behavior analysis and performance improvement in the robot. This paper presents a novel methodology for the inverse dynamic analysis of the heavy-duty industrial robot with elastic joints. In particular, high-order kinematics and dynamics are concisely deduced using Lie group to deal with elastic joints for the robot inverse dynamic analysis. Meanwhile, position errors of the end-effector due to elastic joints are evaluated through the inverse dynamic analysis when the robot is in heavy-duty applications. Compared with previous approaches, the advantage of proposed method is that new formulas for inverse dynamic analysis are shown to be more concise and computationally efficient using Lie group. Moreover, the position error evaluation method considering dynamic forces is proved to be more accurate than the traditional method when the robot is in the high-speed application. Because of the high computational efficiency and accurate evaluation results, the proposed approach is applicable to trajectory optimization and position error compensation, especially for the robot in heavy-load and high-speed applications.

Keywords

Inverse dynamic analysis Lie group Position errors Heavy-duty industrial robot Elastic joints 

Notes

Acknowledgements

This work is supported by the Major State Basic Research Development Program of China (973 Program, Grant No. 2014CB046704) and National Science and Technology Support Plan (Grant No. 2014BAB13B01).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest concerning the publication of this manuscript.

References

  1. 1.
    You, W., Kong, M., Sun, L., Diao, Y.: Control system design for heavy duty industrial robot. Ind. Robot: Int. J. 39(4), 365–380 (2012)CrossRefGoogle Scholar
  2. 2.
    Guo, Y., Dong, H., Wang, G., Ke, Y.: Vibration analysis and suppression in robotic boring process. Int. J. Mach. Tools Manuf. 101, 102–110 (2016)CrossRefGoogle Scholar
  3. 3.
    Zivanovic, S., Slavkovic, N., Milutinovic, D.: An approach for applying STEP-NC in robot machining. Robot. Comput. Integr. Manuf. 49, 361–373 (2018)CrossRefGoogle Scholar
  4. 4.
    Guillo, M., Dubourg, L.: Impact & improvement of tool deviation in friction stir welding: weld quality & real-time compensation on an industrial robot. Robot. Comput. Integr. Manuf. 39(5), 22–31 (2016)CrossRefGoogle Scholar
  5. 5.
    Backer, J.D., Christiansson, A., Oqueka, J., Bolmsjö, G.: Investigation of path compensation methods for robotic friction stir welding. Ind. Robot: Int. J. 39(6), 601–608 (2012)CrossRefGoogle Scholar
  6. 6.
    Backer, J.D.: Feedback Control of Robotic Friction Stir Welding. Ph.D. Thesis, University West (2014)Google Scholar
  7. 7.
    Mendes, N., Neto, P., Loureiro, A., Moreira, A.P.: Machines and control systems for friction stir welding: a review. Mater. Des. 90, 256–265 (2016)CrossRefGoogle Scholar
  8. 8.
    Belchior, J., Guillo, M., Courteille, E., Maurine, P., Leotoing, L., Guines, D.: Off-line compensation of the tool path deviations on robotic machining: application to incremental sheet forming. Robot. Comput. Integr. Manuf. 29(4), 58–69 (2013)CrossRefGoogle Scholar
  9. 9.
    Klimchik, A., Pashkevich, A., Chablat, D., Hovland, G.: Compliance error compensation technique for parallel robots composed of non-perfect serial chains. Robot. Comput. Integr. Manuf. 29(2), 385–393 (2013)CrossRefGoogle Scholar
  10. 10.
    Klimchik, A., Pashkevich, A.: Serial vs. quasi-serial manipulators: comparison analysis of elasto-static behaviors. Mech. Mach. Theory. 107, 46–70 (2017)CrossRefGoogle Scholar
  11. 11.
    Klimchik, A., Furet, B., Caro, S., Pashkevich, A.: Identification of the manipulator stiffness model parameters in industrial environment. Mech. Mach. Theory 90, 1–22 (2015)CrossRefGoogle Scholar
  12. 12.
    Bres, A., Monsarrat, B., Dubourg, L., Birglen, L., Perron, C., Jahazi, M., Baron, L.: Simulation of friction stir welding using industrial robots. Ind. Robot: Int. J. 37(1), 36–50 (2010)CrossRefGoogle Scholar
  13. 13.
    Bianco, C.G.L., Gerelli, O.: Online trajectory scaling for manipulators subject to high-order kinematic and dynamic constraints. IEEE Trans. Robot. 27(6), 1144–1152 (2011)CrossRefGoogle Scholar
  14. 14.
    Colleoni, D., Miceli, G., Pasquarello, A.: Workpiece placement optimization for machining operations with a KUKA KR270-2 robot. In: IEEE International Conference on Robotics and Automation, pp. 6–10 (2013)Google Scholar
  15. 15.
    Macfarlane, S., Croft, E.: Jerk-bounded manipulator trajectory planning: design for real-time applications. IEEE Trans. Robot. Autom. 19(1), 42–52 (2003)CrossRefGoogle Scholar
  16. 16.
    Bianco, C.G.L.: Evaluation of generalized force derivatives by means of a recursive Newton–Euler approach. IEEE Trans. Robot. 25(4), 954–959 (2009)CrossRefGoogle Scholar
  17. 17.
    Spong, M.W.: Modeling and control of elastic joint robots. J. Dyn. Syst. Meas. Control 109(4), 310–318 (1987)CrossRefMATHGoogle Scholar
  18. 18.
    Alici, G., Shirinzadeh, B.: Enhanced stiffness modeling, identification and characterization for robot manipulators. IEEE Trans. Robot. 21(4), 554–564 (2005)CrossRefMATHGoogle Scholar
  19. 19.
    Dumas, C., Caro, S., Garnier, S., Furet, B.: Joint stiffness identification of six-revolute industrial serial robots. Robot. Comput. Integr. Manuf. 27(4), 881–888 (2011)CrossRefGoogle Scholar
  20. 20.
    Luca, A.D., Siciliano, B., Zollo, L.: PD control with on-line gravity compensation for robots with elastic joints: theory and experiments. Automatica 41(10), 1809–1819 (2005)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Ott, C., Albu-Schaffer, A., Kugi, A., Hirzinger, G.: On the passivity-based impedance control of flexible joint robots. IEEE Trans. Robot. 24(2), 416–429 (2008)CrossRefGoogle Scholar
  22. 22.
    Nanos, K., Papadopoulos, E.G.: On the dynamics and control of flexible joint space manipulators. Cont. Eng. Pract. 45, 230–243 (2015))Google Scholar
  23. 23.
    Hopler, R., Thümmel, M.: Symbolic computation of the inverse dynamics of elastic joint robots. In: IEEE International Conference on Robotics and Automation, pp. 4314-4319 (2004Google Scholar
  24. 24.
    Buondonno, G., Luca, A.D.: A recursive Newton–Euler algorithm for robots with elastic joints and its application to control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5526–5532 (2015)Google Scholar
  25. 25.
    Buondonno, G., Luca, A.D.: Efficient computation of inverse dynamics and feedback linearization for VSA-based robots. IEEE Robot. Autom. Lett. 1(2), 908–915 (2016)CrossRefGoogle Scholar
  26. 26.
    Park, F.C., Bobrow, J.E., Ploen, S.R.: A Lie group formulation of robot dynamics. Int. J. Robot. Res. 14(6), 609–618 (1995)CrossRefGoogle Scholar
  27. 27.
    Selig, J.: Geometrical Methods in Robotics. Springer, New York (2013)MATHGoogle Scholar
  28. 28.
    Craig, J.: Introduction to Robotics: Mechanics and Control. Addison-Wesley, Reading (2005)Google Scholar
  29. 29.
    Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer, Berlin (2016)CrossRefMATHGoogle Scholar
  30. 30.
    Chen, I., Yang, G.: Automatic model generation for modular reconfigurable robot dynamics. J. Dyn. Syst. Meas. Control. 120(3), 346–352 (1998)CrossRefGoogle Scholar
  31. 31.
  32. 32.
    Googol Technique, G.T.: http://www.googoltech.com

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

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