Advertisement

Autonomous Robots

, Volume 43, Issue 6, pp 1453–1471 | Cite as

Validating multi-rigid body simulation of a wild robot

  • James R. TaylorEmail author
  • Evan Drumwright
Article
  • 127 Downloads

Abstract

There exist few objective measures to evaluate or compare multi-rigid body dynamics simulations involving contact and friction. This absence creates uncertainty in simulation capabilities and accuracy, leaving users to wonder when can they trust simulations. Simulation science has focused on using theory and other simulations (verification) and real-world data (validation) to evaluate simulation correctness. With respect to rigid body dynamics, ballistic rigid body motion has been verified and validated, but rigid body simulations involving contact and friction are currently prone to producing results that appear inconsistent with real-world observations. Accurate validation is seldom performed for contacting “rigid” bodies, likely because the observation problem is so challenging (compared to, e.g., fluid dynamics, for which fluids are often transparent). This paper concentrates on a validation scenario for multi-rigid body dynamics with contact and friction, which are essential for simulating robotic locomotion and manipulation. We describe a collection and estimation process for telemetry data of a mechanically simple but highly dynamic, real-world robot whose motion is primarily driven by contact and friction, and we propose an approach for quantifying the performance of simulations of this robot.

Keywords

Simulation validation Motion capture Underactuated robots 

Notes

Acknowledgements

We would like to acknowledge Jack Shannon, Roxana Leontie, Jon Torrey, Paul Mitiguy, Robert Pless, Richard Taylor, and our anonymous reviewers for their assistance with this work.

Supplementary material

References

  1. Bobadilla, L., Martinez, F., Gobst, E., Gossman, K., & Lavalle, S. M. (2012). Controlling wild mobile robots using virtual gates and discrete transitions. In Proceedings of American control conference (ACC), IEEE (pp. 743–749). Canada: Montréal.Google Scholar
  2. Bobadilla, L., Sanchez, O., Czarnowski, J., Gossman, K., & LaValle, S. M. (2011). Controlling wild bodies using linear temporal logic. In Proceedings of robotics: Science and systems (RSS) VII (pp 17–24). Los Angeles, USA: MIT Press.Google Scholar
  3. Boeing, A., & Bräunl, T. (2007). Evaluation of real-time physics simulation systems. In Proceedings of the 5th International Conference on Computer Graphics and Interactive Techniques in Australia and Southeast Asia, GRAPHITE ’07 (pp. 281–288).Google Scholar
  4. Chatterjee, A., & Ruina, A. (1998). A new algebraic rigid-body collision law based on impulse space considerations. Journal of Applied Mechanics, 65(64), 939–951.CrossRefGoogle Scholar
  5. Drucker, H., Burges, C. J., Kaufman, L., Smola, A. J., & Vapnik, V. (1997), Support vector regression machines. In Advances in neural information processing systems (pp 155–161).Google Scholar
  6. Erez, T., Tassa, Y., & Todorov, E. (2015). Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 4397–4404).Google Scholar
  7. Fazeli, N., Donlon, E., Drumwright, E., & Rodriguez, A. (2017). Empirical evaluation of common contact models for planar impact. In 2017 IEEE international conference on robotics and automation (ICRA) (pp. 3418–3425).Google Scholar
  8. Frigerio, M., Barasuol, V., Focchi, M., Caldwell, D. G., & Semini, C. (2017). Validation of computer simulations of the HyQ robot. In Proceedings of international conference on climbing walking robots (CLAWAR).Google Scholar
  9. Gierl, D.E., Bobadilla, L., Sanchez, O., & Lavalle, S. M. (2014). Stochastic modeling, control, and verification of wild bodies. In Proceedings of IEEE international conference on robotics and automation (ICRA) (pp. 549–556). Hong Kong, China: IEEE.Google Scholar
  10. González, M., Dopico, D., Lugrís, U., & Cuadrado, J. (2006). A benchmarking system for MBS simulation software: Problem standardization and performance measurement. Multibody System Dynamics, 16(2), 179–190.CrossRefzbMATHGoogle Scholar
  11. González, M., González, F., Luaces, A., & Cuadrado, J. (2009). A collaborative benchmarking framework for multibody system dynamics. Engineering with Computers, 26(1), 1–9.CrossRefGoogle Scholar
  12. Güèmez, J., Valiente, R., Fiolhais, C., & Fiolhais, M. (2003). Experiments with the drinking bird. American Journal of Physics, 71, 1257–1263.CrossRefGoogle Scholar
  13. Ivanov, A. P. (1995). On multiple impact. Journal of Applied Mathematics and Mechanics, 59(6), 887–902.MathSciNetCrossRefzbMATHGoogle Scholar
  14. Kolbert, R., Dafle, N. C., Rodriguez, A., & (2016). Experimental validation of contact dynamics for in-hand manipulation. In: 2016 IEEE international symposium on experimental robotics (ISER). Japan: Tokyo.Google Scholar
  15. Lu, Y., Williams, J., Trinkle, J., & Lacoursire, C. (2014). A framework for problem standardization and algorithm comparison in multibody system. In 10th international conference on multibody systems, nonlinear dynamics, and control, IDETC/CIE 2014 (Vol. 6).Google Scholar
  16. Mitiguy, P. C., & Banerjee, A. K. (1999). Efficient simulation of motions involving Coulomb friction. Journal of Guidance, Control, and Dynamics, 22(1), 78–86.CrossRefGoogle Scholar
  17. Pfeiffer, F. (1984). Mechanische systeme mit unstetigen übergängen. Ingenieur-Archiv, 54(3), 232–240.CrossRefzbMATHGoogle Scholar
  18. Pfeiffer, F., & Glocker, C. (1996). Multibody dynamics with unilateral contacts. New York, NY: Wiley.CrossRefzbMATHGoogle Scholar
  19. Ruina, A., & Pratap, R. (1994). Introduction to statics and dynamics. Oxford: Oxford University Press.Google Scholar
  20. Schlesinger, S., Crosbie, R. E., Gagné, R. E., Innis, G. S., Lalwani, C., Loch, J., et al. (1979). Terminology for model credibility. Simulation, 32(3), 103–104.CrossRefGoogle Scholar
  21. Taylor, J. R., & Drumwright, E. (2016). State estimation of a wild robot towards simulator validation. In 2016 IEEE international conference on simulation, modeling, and programming for autonomous robots (SIMPAR) (pp. 310–317). San Francisco, USA: IEEE.Google Scholar
  22. Ylikorpi, T., & Suomela, J. (2007). Ball-shaped robots. In H. Zhang (Ed.), Climbing & walking robots, toward new applications, chap 11 (pp. 546–567). Vienna, Austria: Itech Education and Publishing.Google Scholar
  23. Yu, K., Bauzá, M., Fazeli, N., & Rodriguez, A. (2016). More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing. In 2016 IEEE/RSJ international conference on intelligent robots and systems, IROS 2016, Daejeon, South Korea, October 9–14, 2016 (pp 30–37).Google Scholar
  24. Zhang, L., Betz, J., & Trinkle, J.C. (2010). Comparison of simulated and experimental grasping actions in the plane. In First joint international conference on multibody system dynamics.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.George Washington UniversityWashingtonUSA
  2. 2.Toyota Research InstitutePalo AltoUSA

Personalised recommendations