Skip to main content
SpringerLink
Log in

A Fast and Unified Method to Find a Minimum-Jerk Robot Joint Trajectory Using Particle Swarm Optimization

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In robot trajectory planning, finding the minimum-jerk joint trajectory is a crucial issue in robotics because most robots are asked to perform a smooth trajectory. Jerk, the third derivative of joint position of a trajectory, influences how smoothly and efficiently a robot moves. Thus, the minimum-jerk joint trajectory makes the robot control algorithm simple and robust. To find the minimum-jerk joint trajectory, it has been formulated as an optimization problem constrained by joint inter-knot parameters including initial joint displacement and velocity, intermediate joint displacement, and final joint displacement and velocity. In this paper, we propose a fast and unified approach based on particle swarm optimization (PSO) with K-means clustering to solve the near-optimal solution of a minimum-jerk joint trajectory. This work differs from previous work in its fast computation and unified methodology. Computer simulations were conducted and showed the competent performance of our approach on a six degree-of-freedom robot manipulator.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Asakawa, N., Takeuchi, Y.: Teachingless spray-painting of sculptured surface by an industrial robot. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 1875–1879 (1997)

  2. Barre, P.J., Bearee, R., Borne, P., Dumetz, E.: Influence of a jerk controlled movement law on the vibratory behaviour of high-dynamics systems. J. Intell. Robot. Syst. 42, 275–293 (2005)

    Google Scholar 

  3. Brogardh, T.: Present and future robot control developmentan industrial perspective. Annu. Rev. Control 31(1), 69–79 (2007)

    Google Scholar 

  4. Chettibia, T., Lehtiheta, H.E., Haddada, M., Hanchib, S.: Minimum cost trajectory planning for industrial robots. Eur. J. Mech. 23(4), 703–715 (2004)

    Google Scholar 

  5. Choi, S., Newman, W.S.: Design and evaluation of a laser-cutting robot for laminated, solid freeform fabrication. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 1551–1556 (2000)

  6. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Google Scholar 

  7. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm. In: Proc. Congr. Evol. Comput., pp. 84–88 (2000)

  8. Engelbrecht, S.E.: Minimum principles in motor control. J. Math. Psychol. 45(3), 497–542 (2001)

    MATH  MathSciNet  Google Scholar 

  9. Evers, G.I., Ghalia, M.B.: Regrouping particle swarm optimization: a new global optimization algorithm with improved performance consistency across benchmarks. In: IEEE Int. Conf. System., Man and Cybernetics, pp. 3901–3908 (2009)

  10. Gasparetto, A., Zanotto, V.: A technique for time-jerk optimal planning of robot trajectories. Robot. Comput. Integr. Manuf. 24, 415–426 (2008)

    Google Scholar 

  11. Huang, P., Xu, Y., Liang, B.: Global minimum-jerk trajectory planning of space manipulator. Int. J. Control Autom. Syst. 4(4), 405–413 (2006)

    Google Scholar 

  12. Huang, P., Xu, Y., Liang, B.: Minimum-torque path planning of space robots using genetic algorithms. Int. J. Robot. Autom. 21(3), 229–236 (2006)

    Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. Int. Conf. Neural Networks, pp. 1942–1948 (1995)

  14. Kim, D.H., Shin, S.: Self-organization of decentralized swarm agents based on modified particle swarm algorithm. J. Intell. Robot. Syst. 46, 129–149 (2006)

    Google Scholar 

  15. Kyriakopoulos, K.J., Saridis, G.N.: Minimum jerk path generation. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 364–369 (1988)

  16. Lee, S.H., Kim, J., Park, F.C., Kim, M., Bobro, J.E.: Newton-type algorithms for dynamics-based robot movement optimization. IEEE Trans. Robot. 21(4), 657–667 (2005)

    Google Scholar 

  17. Lin, C.S., Chang, P.R., Luh, J.Y.S.: Formulation and optimization of cubic polynomial joint trajectories for industrial robots. IEEE Trans. Autom. Control 28(12), 1066–1073 (1983)

    MATH  Google Scholar 

  18. Nagata, F., Watanabe, K., Kiguchi, K., Tsuda, K., Kawaguchi, S., Noda, Y., Komino, M.: Design and evaluation of a laser-cutting robot for laminated, solid freeform fabrication. In: Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 362–367 (2001)

  19. Piazzi, A., Visioli, A.: Global minimum-jerk trajectory planning of robot manipulators. Int. J. Robot. Autom. 47(1), 140–149 (2000)

    Google Scholar 

  20. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proc. IEEE Int. Conf. Evol. Comput., pp. 69–73 (1998)

  21. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proc. Congr. Evol. Comput., pp. 1945–1950 (1999)

  22. Shin, K.G., McKay, N.D.: Minimum-time control of robotic manipulators with geometric path constraints. IEEE Trans. Autom. Control 30(6), 531–541 (1985)

    MATH  Google Scholar 

  23. Simon, D., Isik, C.: A trigonometric trajectory generator for robotic arms. Int. J. Control 57(3), 505–517 (1993)

    MATH  MathSciNet  Google Scholar 

  24. Simons, J., Brussel, H., Schutter, J.D., Verhaert, J.: A self-learning automaton with variable resolution for high precision assembly by industrial robots. IEEE Trans. Autom. Control 27(5), 1109–1113 (1982)

    MATH  Google Scholar 

  25. Smith, C.B.: Robotic friction stir welding using a standard industrial robot. J. Light Met. Weld. Constr. 42(3), 40–41 (2004)

    Google Scholar 

  26. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)

    MATH  MathSciNet  Google Scholar 

  27. Viviani, P., Flash, T.: Formulation and optimization of cubic polynomial joint trajectories for industrial robots. J. Exp. Psychol. 21(1), 32–53 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate Institute of Automation Technology, National Taipei University of Technology, Rm 801A, Integrated Technology Complex, 1, Sec. 3, Chung-Hsiao E. Road, Taipei, 10608, Taiwan

    Hsien-I Lin

Authors
  1. Hsien-I Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hsien-I Lin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lin, HI. A Fast and Unified Method to Find a Minimum-Jerk Robot Joint Trajectory Using Particle Swarm Optimization. J Intell Robot Syst 75, 379–392 (2014). https://doi.org/10.1007/s10846-013-9982-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-013-9982-8

Keywords

Search

Navigation