Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification

Research output: Contribution to journal › Article › Research › peer review

Authors

  • Yanan Li
  • Gowrishankar Ganesh
  • Nathanael Jarrasse
  • Sami Haddadin

Research Organisations

External Research Organisations

  • Imperial College London
  • University of Sussex
  • National Institute of Advanced Industrial Science and Technology
  • Laboratoire de Robotique de Paris (LRP)
  • German Aerospace Center (DLR)
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Details

Original languageEnglish
Article number8362715
Pages (from-to)1170-1182
Number of pages13
JournalIEEE transactions on robotics
Volume34
Issue number5
Early online date22 May 2018
Publication statusPublished - 5 Oct 2018

Abstract

Humans can skilfully use tools and interact with the environment by adapting their movement trajectory, contact force, and impedance. Motivated by the human versatility, we develop here a robot controller that concurrently adapts feedforward force, impedance, and reference trajectory when interacting with an unknown environment. In particular, the robot's reference trajectory is adapted to limit the interaction force and maintain it at a desired level, while feedforward force and impedance adaptation compensates for the interaction with the environment. An analysis of the interaction dynamics using Lyapunov theory yields the conditions for convergence of the closed-loop interaction mediated by this controller. Simulations exhibit adaptive properties similar to human motor adaptation. The implementation of this controller for typical interaction tasks including drilling, cutting, and haptic exploration shows that this controller can outperform conventional controllers in contact tooling.

Keywords

    Adaptive control, biological systems control, contact tasks, force control, iterative learning control, robot control

ASJC Scopus subject areas

Cite this

Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification. / Li, Yanan; Ganesh, Gowrishankar; Jarrasse, Nathanael et al.
In: IEEE transactions on robotics, Vol. 34, No. 5, 8362715, 05.10.2018, p. 1170-1182.

Research output: Contribution to journal › Article › Research › peer review

Li, Y, Ganesh, G, Jarrasse, N, Haddadin, S, Albu-Schaeffer, A & Burdet, E 2018, 'Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification', IEEE transactions on robotics, vol. 34, no. 5, 8362715, pp. 1170-1182. https://doi.org/10.1109/TRO.2018.2830405
Li, Y., Ganesh, G., Jarrasse, N., Haddadin, S., Albu-Schaeffer, A., & Burdet, E. (2018). Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification. IEEE transactions on robotics, 34(5), 1170-1182. Article 8362715. https://doi.org/10.1109/TRO.2018.2830405
Li Y, Ganesh G, Jarrasse N, Haddadin S, Albu-Schaeffer A, Burdet E. Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification. IEEE transactions on robotics. 2018 Oct 5;34(5):1170-1182. 8362715. Epub 2018 May 22. doi: 10.1109/TRO.2018.2830405
Li, Yanan ; Ganesh, Gowrishankar ; Jarrasse, Nathanael et al. / Force, Impedance, and Trajectory Learning for Contact Tooling and Haptic Identification. In: IEEE transactions on robotics. 2018 ; Vol. 34, No. 5. pp. 1170-1182.
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