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Iterative Learning-Based Admittance Control for Autonomous Excavation

  • Heshan FernandoEmail author
  • Joshua A. Marshall
  • Johan Larsson
Article
  • 27 Downloads

Abstract

This paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy is augmented with iterative learning, which automatically updates control parameters based on the error between a target bucket fill weight and the measured fill weight at the end of each excavation pass. The algorithm was validated through full-scale autonomous excavation experiments with a 14-tonne capacity load-haul-dump (LHD) machine and two different types of excavation materials: fragmented rock and gravel. In both excavation scenarios, the iterative learning algorithm is able to update the admittance control parameters for a specified target bucket fill weight, eliminating the need to manually re-tune control parameters as material characteristics change. These results have practical significance for increasing the autonomy of robotic wheel loaders used in mining and construction.

Keywords

Autonomous excavation Iterative learning Admittance control Mining robotics 

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Notes

Acknowledgements

The authors would like to thank Håkan Almqvist for his assistance with the field experiments; as well as Ola Petersson, Jörgen Appelgren and Robert Lundh for many useful discussions; and Qing Huang for assistance with programming.

References

  1. 1.
    Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control. IEEE Control. Syst. Mag. 26(3), 96–114 (2006)CrossRefGoogle Scholar
  2. 2.
    Buchli, J., Stulip, F., Theodorou, E., Schaal, S.: Learning variable impedance control. Int. J. Robot. Res. 30(7), 820–833 (2011)CrossRefGoogle Scholar
  3. 3.
    Dadhich, S., Bodin, U., Andersson, U.: Key challenges in automation of earth-moving machines. Autom. Constr. 68, 212–222 (2016)CrossRefGoogle Scholar
  4. 4.
    Dadhich, S., Bodin, U., Sandin, F., Andersson, U.: Machine learning approach to automatic bucket fill. In: Proceedings of 24th Mediterranean Conference on Control and Automation, Athens, pp. 1260–1265 (2016)Google Scholar
  5. 5.
    Dadhich, S., Sandin, F., Bodin, U., Andersson, U., Martinsson, T.: Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders. Autom. Construct. 97, 1–12 (2019).  https://doi.org/10.1016/j.autcon.2018.10.013 CrossRefGoogle Scholar
  6. 6.
    Dimeas, F., Aspragathos, N.: Reinforcement learning of variable admittance control for human-robot co-manipulation. In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, pp. 1011–1016 (2015)Google Scholar
  7. 7.
    Dobson, A.A., Marshall, J.A., Larsson, J.: Admittance control for robotic loading: Design and experiments with a 1-tonne loader and a 14-tonne LHD. Invited Paper, Special Issue Field Serv. Robot. J. Field Robot. 34(1), 123–150 (2017)Google Scholar
  8. 8.
    Fernando, H., Marshall, J.A., Almqvist, H., Larsson, J.: Towards Controlling Bucket Fill Factor in Robotic Excavation by Learning Admittance Control Setpoints. In: Hutter, M., Siegwart, R. (eds.) Field and Service Robotics: Results of the 11Th International Conference, pp. 35–48, Zürich (2017)Google Scholar
  9. 9.
    Goska, W.J., Muller, J.Y., Gaibler, D.W., Thompson, D., Lawe, D.A., Van Schoiack, M.M.: Computerized monitoring management system for load carrying vehicle, vol. 5 (1997)Google Scholar
  10. 10.
    Haddadin, S., Croft, E.: Springer Handbook of Robotics, 2 Edn., Chap. 69–Physical Human-Robot Interaction. Springer International Publishing, Switzerland, pp. 1835–1874 (2016)Google Scholar
  11. 11.
    Hemami, A., Hassani, F.: An overview of autonomous loading of bulk material. In: Proceedings of the 26th International Symposium on Automation Robotics in Construction, Austin, pp. 405–411 (2009)Google Scholar
  12. 12.
    Jiménez, M.F., Monllor, M., Frizera, A., Bastos, T., Roberti, F., Carelli, R.: Admittance controller with spatial modulation for assisted locomotion using a smart walker. Journal of Intelligent & Robotic Systems.  https://doi.org/10.1007/s10846-018-0854-0 (2018)
  13. 13.
    Kim, Y.J., Seo, J., Kim, H., Kim, K.G.: Impedance and admittance control for respiratory-motion compensation during robotic needle insertion – a preliminary test. The International Journal of Medical Robotic and Computer Assisted Surgery 13(3), 1–10 (2016)Google Scholar
  14. 14.
    Li, Y., Ge, S.: Impedance learning for robots interacting with unknown envirnonments. IEEE Trans. Control Syst. Technol. 22(4), 1422–1432 (2014)CrossRefGoogle Scholar
  15. 15.
    Maeda, G.J., Manchester, I.R., Rye, D.C.: Combined ILC and disturbance observer for the rejection of near-repetitive disturbances, with application to excavation. IEEE Trans. Control Syst. Technol. 23(5), 1754–1769 (2015)CrossRefGoogle Scholar
  16. 16.
    Marshall, J.A., Murphy, P.F., Daneshmend, L.K.: Toward autonomous excavation of fragmented rock: full-scale experiments. IEEE Trans. Autom. Sci. Eng. 5(3), 562–566 (2008)CrossRefGoogle Scholar
  17. 17.
    Seraji, H.: Adaptive admittance control: an approach to explicit force control in compliant motion. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, pp. 2705–2712 (1994)Google Scholar
  18. 18.
    Villani, L., De Schutter, L.: Springer Handbook of Robotics, 2 edn., chap. 9–Force Control. Springer International Publishing, Switzerland, pp. 195–220 (2016)Google Scholar
  19. 19.
    Wang, C., Li, Y., Ge, S.S., Lee, T.H.: Reference adaptation for robots in physical interactions with unknown environments. IEEE Trans. Cybern. 47(11), 3504–3515 (2017)CrossRefGoogle Scholar
  20. 20.
    Yamawaki, T., Ishikawa, H., Yashima, M.: Iterative learning of variable impedance control for human-robot cooperation. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, pp. 839–844 (2016)Google Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Mechanical and Materials EngineeringQueen’s University at KingstonKingstonCanada
  2. 2.Faculty of Engineering and Applied ScienceQueen’s University at KingstonKingstonCanada
  3. 3.Rocktec DivisionEpiroc Rock Drills AB at ÖrebroÖrebroSweden

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