Multibody System Dynamics

, Volume 37, Issue 2, pp 175–193 | Cite as

Explicit formulation of multibody dynamics based on principle of dynamical balance and its parallelization

Article
  • 215 Downloads

Abstract

Efficient computation of dynamics parameters is one of the important issues in simulation and control of the multibody systems as these systems become more complex. Recent advances in computer architecture are toward multiple core systems rather than high-speed single core systems. Therefore, parallel computation algorithms for dynamics parameters should be designed to improve the performance on these multicore architectures. In this paper, a new dynamics computation algorithm is derived using the principle of dynamical balance, which provides explicit computation of dynamic parameters. This new algorithm has the structure to which parallel computation can be easily applicable. Parallel computation methods are then applied so that we can exploit the structure of the proposed dynamics computation algorithm based on the principle of dynamical balance. The parallel algorithm is designed based on task and data-parallelism. The performance of the proposed algorithm is verified on robots with various topologies. The improved speed of parallel computation is demonstrated through these experiments.

Keywords

Multibody system dynamics Principle of dynamical balance Parallel computing 

Notes

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A1A10055798) and the Technology Innovation Program (10060081) funded by the Ministry of Trade, industry & Energy (MI, Korea).

References

  1. 1.
    Stepanenko, Y., Vukobratovic, M.: Dynamics of articulated open-chain active mechanisms. Math. Biosci. 28, 137–170 (1976) MathSciNetCrossRefGoogle Scholar
  2. 2.
    Orin, D.E., McGhee, R.B., Vukobratovic, M., Hartoch, G.: Kinematic and kinetic analysis of open-chain linkages utilizing Newton–Euler methods. Math. Biosci. 43, 107–130 (1979) CrossRefMATHGoogle Scholar
  3. 3.
    Luh, J.Y.S., Walker, M.W., Paul, R.P.C.: On-line computational scheme for mechanical manipulators. J. Dyn. Syst. Meas. Control 102(2), 69–76 (1980) MathSciNetCrossRefGoogle Scholar
  4. 4.
    Featherstone, R.: The calculation of robot dynamics using articulated-body inertias. Int. J. Robot. Res. 2(1), 13–30 (1983) CrossRefGoogle Scholar
  5. 5.
    Featherstone, R.: Robot Dynamics Algorithms. Kluwer Academic, Boston/Dordrecht/Lancaster (1987) Google Scholar
  6. 6.
    Lilly, K.W., Orin, D.E.: Alternate formulations for the manipulator inertia matrix. Int. J. Robot. Res. 10, 64–74 (1991) CrossRefGoogle Scholar
  7. 7.
    From, J.: An explicit formulation of singularity-free dynamic equations of mechanical systems in Lagrangian form—part one: single rigid bodies. Model. Identif. Control 33(2), 45–60 (2012) CrossRefGoogle Scholar
  8. 8.
    Fijany, A., Bejczy, A.K.: A class of parallel algorithms for computation of the manipulator inertia matrix. IEEE Trans. Robot. Autom. 5(5), 600–615 (1989) CrossRefGoogle Scholar
  9. 9.
    Lee, C.S.G., Chang, P.R.: Efficient parallel algorithms for robot forward dynamics computation. IEEE Trans. Syst. Man Cybern. 18(2), 238–251 (1988) MathSciNetCrossRefGoogle Scholar
  10. 10.
    Bondhugula, U., Baskaran, M.M., Hartono, A., Krishnamoorthy, S., Ramanujam, J., Rountev, A., Sadayappan, P.: Towards effective automatic parallelization for multicore systems. In: IPDPS. IEEE, pp. 1–5 (2008) Google Scholar
  11. 11.
    Agathos, S.N., Hadjidoukas, P.E., Dimakopoulos, V.V.: Design and implementation of openmp tasks in the ompi compiler. In: Angelidis, P., Michalas, A. (eds.) Panhellenic Conference on Informatics, pp. 265–269. IEEE Press, New York (2011) Google Scholar
  12. 12.
    Duan, S., Anderson, K.S.: Parallel implementation of a low order algorithm for dynamics of multibody systems on a distributed memory computing system. Eng. Comput. 16(2), 96–108 (2000) CrossRefMATHGoogle Scholar
  13. 13.
    Chhugani, J., Nguyen, A.D., Lee, V.W., Macy, W., Hagog, M., Chen, Y.-K., Baransi, A., Kumar, S., Dubey, P.: Efficient implementation of sorting on multi-core SIMD CPU architecture. In: Proceedings of the VLDB Endowment, vol. 1, pp. 1313–1324 (2008) Google Scholar
  14. 14.
    Park, J.: Principle of dynamical balance for multibody systems. Multibody Syst. Dyn. 14(3), 269–299 (2005) MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Murray, R.M., Li, Z., Sastry, S.S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994) MATHGoogle Scholar
  16. 16.
    You, B., Choi, Y., Jeong, M., Kim, D., Oh, Y., Kim, C., Cho, J., Park, M., Oh, S.: Network-based humanoids ‘Marhu’ and ‘Ahra’. In: Proc. Int. Conf. on Ubi. Robots and Ambient Intelli, pp. 376–379 (2005) Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Graduate School of Convergence Science and TechnologySeoul National UniversitySeoulRepublic of Korea
  2. 2.Neuromeka, 406 Seongsu IT CenterSeoulRepublic of Korea
  3. 3.Advanced Institutes of Convergence TechnologySeoul National UniversitySeoulRepublic of Korea

Personalised recommendations