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Fundamental Limitations in Performance and Interpretability of Common Planar Rigid-Body Contact Models

  • Nima FazeliEmail author
  • Samuel Zapolsky
  • Evan Drumwright
  • Alberto Rodriguez
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

The ability to reason about and predict the outcome of contacts is paramount to the successful execution of many robot tasks. Analytical rigid-body contact models are used extensively in planning and control due to their computational efficiency and simplicity, yet despite their prevalence, little if any empirical comparison of these models has been made and it is unclear how well they approximate contact outcomes. In this paper, we first formulate a system identification approach for six commonly used contact models in the literature, and use the proposed method to find parameters for an experimental data-set of impacts. Next, we compare the models empirically, and establish a task specific upper bound on the performance of the models and the rigid-body contact model paradigm. We highlight the limitations of these models, salient failure modes, and the care that should be taken in parameter selection, which are ultimately difficult to give a physical interpretation.

References

  1. 1.
    Anitescu, M., Potra, F.A.: Formulating dynamic multi-rigid-body contact problems with friction as solvable linear complementarity problems. Nonlinear Dyn. 14, 231–247 (1997)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Chatterjee, A., Ruina, A.: A new algebraic rigid body collision law based on impulse space considerations. ASME J. Appl. Mech. 65(4), 939–951 (1998)CrossRefGoogle Scholar
  3. 3.
    Chavan Dafle, N., Rodriguez, A .: Prehensile pushing: in-hand manipulation with push-primitives. In: IEEE/RSJ International Conference on Intelligent Robots Systems (IROS), pp. 6215 – 6222 (2015)Google Scholar
  4. 4.
    Drumwright, E., Shell, D.A.: Modeling contact friction and joint friction in dynamic robotic simulation using the principle of maximum dissipation. In: Proceedings of Workshop on the Algorithmic Foundations of Robotics (WAFR), pp. 249–266. Springer, Berlin (2010)Google Scholar
  5. 5.
    Fazeli, N., Tedrake, R., Rodriguez, A.: Identifiability analysis of planart rigid-body frictional contact. In: International Symposium Robotics Research (ISRR), pp. 665–682. Springer, Cham (2016)Google Scholar
  6. 6.
    Fazeli, N., Donlon, E., Drumwright, E., Rodriguez, A.: Empirical evaluation of common contact models for planar impact. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3418–3425 (2017)Google Scholar
  7. 7.
    Fazeli, N., Kolbert, R., Tedrake, R., Rodriguez, A.: Parameter and contact force estimation of planar rigid-bodies undergoing frictional contact. Int. J. Robot. Res. 36(13–14), 1437–1454 (2017)Google Scholar
  8. 8.
    Fazeli, N., Zapolsky, S., Drumwright, E., Rodriguez, A.: Learning data-efficient rigid-body contact models: case study of planar impact (2017). arXiv:171005947
  9. 9.
    Gautier, M., Khalil, W.: On the identification of the inertial parameters of robots. In: IEEE Conference on Decision and Control, pp 2264–2269. Austin (1988)Google Scholar
  10. 10.
    Hertz, H.: On the contact of elastic solids. J. Reine Angew. Math. 92, 156–171 (1881)zbMATHGoogle Scholar
  11. 11.
    Hogan, F., Rodriguez, A.: Feedback control of the pusher-slider system: a story of hybrid and underactuated contact dynamics. In: Proceedings of the Workshop on Algorithmic Foundation Robotics (WAFR). San Francisco (2016)Google Scholar
  12. 12.
    Kane, T.R., Levinson, D.A.: Dynamics: Theory and Applications. McGraw-Hill, New York (1985)Google Scholar
  13. 13.
    Khosla, P., Kanade, T.: Parameter identification of robot dynamics. In: IEEE Conference on Decision and Control, pp. 1754–1760 (1985)Google Scholar
  14. 14.
    Koval, M., Pollard, N., Srinivasa, S.: Pose estimation for planar contact manipulation with manifold particle filters. Int. J. Robot. Res. 7(34), 922–945 (2015)CrossRefGoogle Scholar
  15. 15.
    Kraus, P.R., Kumar, V.: Compliant contact models for rigid body collisions. In: Proceedings of International Conference on Robotics and Automation, vol. 2, pp. 1382–1387 (1997)Google Scholar
  16. 16.
    Lacoursiére, C.: Ghosts and machines: regularized variational methods for interactive simulations of multibodies with dry frictional contacts. Ph.D. thesis, Umeå University, Umeå (2007)Google Scholar
  17. 17.
    Lankarani, H., Nikravesh, P.: A contact force model with hysteresis damping for impact analysis of multibody systems. J. Mech. Design 112(3), 369–376 (1990)CrossRefGoogle Scholar
  18. 18.
    Li, Z., Kota, S.: Virtual prototyping and motion simulation with adams. J. Comput. Inf. Sci. Eng. 1(3), 276–279 (2001)CrossRefGoogle Scholar
  19. 19.
    Marhefka, D.W., Orin, D.E.: A compliant contact model with nonlinear damping for simulation of robotic systems. IEEE Trans. Syst. Man Cybern. 29(6), 566–572 (1999)CrossRefGoogle Scholar
  20. 20.
    Mirtich, B.: Impulse-based dynamic simulation of rigid body systems. Ph.D. thesis, University of California, Berkeley (1996)Google Scholar
  21. 21.
    Nubiola, A., Bonev, I.A.: Absolute calibration of an abb irb 1600 robot using a laser tracker. Robot. Comput. Integr. Manuf. 29(1), 236–245 (2013)CrossRefGoogle Scholar
  22. 22.
    Posa, M., Cantu, C., Tedrake, R.: A direct method for trajectory optimization of rigid bodies through contact. Int. J. Robot. Res. 33(1), 69–81 (2013)CrossRefGoogle Scholar
  23. 23.
    Shapiro, A., Faloutsos, P., Ng-Thow-Hing, V.: Dynamic animation and control environment. In: Proceedings of Graphics Interface 2005, Canadian Human-Computer Communications Society, pp. 61–70 (2005)Google Scholar
  24. 24.
    Slotine, J.J.E.: On the adaptive control of robot manipulators. Int. J. Robot. Res. 6(3), 49–59 (1987)CrossRefGoogle Scholar
  25. 25.
    Song, P., Kraus, P., Kumar, V., Dupont, P.: Analysis of rigid body dynamic models for simulation of systems with frictional contacts. J. Appl. Mech. 68 (2000)Google Scholar
  26. 26.
    Stewart, D.E.: Rigid-body dynamics with friction and impact. SIAM 42, 3–39 (2000)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Stewart, D.E., Trinkle, J.C.: An implicit time-stepping scheme for rigid body dynamics with inelastic collisions and coulomb friction. Int. J. Numer. Methods Eng. 39(15), 2673–2691 (1996)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Stronge, W.J.: Rigid body collisions with friction. Proc. R. Soc. Lond. A 431, 169–181 (1990)Google Scholar
  29. 29.
    Wang, Y., Mason, M.T.: Two-dimensional rigid-body collisions with friction. ASME J. Appl. Mech. 59, 635–642 (1992)CrossRefGoogle Scholar
  30. 30.
    Whittaker, E.T.: A Treatise on the Analytical Dynamics of Particles and Rigid Bodies, 4th edn. Dover, Mineola (1944)Google Scholar
  31. 31.
    Yu, K.T., Bauza. M., Fazeli. N., Rodriguez, A.: More than a million ways to be pushed. a high-fidelity experimental data set of planar pushing. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Nima Fazeli
    • 1
    Email author
  • Samuel Zapolsky
    • 2
  • Evan Drumwright
    • 2
  • Alberto Rodriguez
    • 1
  1. 1.Mechanical Engineering DepartmentMITCambridgeUSA
  2. 2.Toyota Research InstituteLos AltosUSA

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