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JACIII Vol.11 No.8 pp. 956-963
doi: 10.20965/jaciii.2007.p0956
(2007)

Paper:

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Particle Swarm Optimization for Jump Height Maximization of a Serial Link Robot

Takeshi Matsui*, Masatoshi Sakawa*, Takeshi Uno*, Kosuke Kato*,
Mitsuru Higashimori**, and Makoto Kaneko**

*Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashihiroshima-shi 739-8527, Japan

**Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita-shi 565-0871, Japan

Received:
March 15, 2007
Accepted:
May 23, 2007
Published:
October 20, 2007
Creative Commons License
Keywords:
serial link robot, jump height maximization, nonlinear programming, particle swarm optimization
Abstract
In this paper, we focus on the maximization of the height of jump of a serial link robot. The jump height maximization problem is formulated as a nonlinear programming problem, where torque patterns to drive joints in the robot are decision variables and the objective function is an implicit function whose value is obtained as an output of a simulator. As a previous reasearch, an approximate solution method using a genetic algorithm was proposed. In the research, some interesting joint drive torque patterns were found by the method, but it costed much time to obtain a drive torque pattern. In order to shorten the computational time, in this paper, we propose a new solution method using a particle swarm optimization (PSO) technique.
Cite this article as:
T. Matsui, M. Sakawa, T. Uno, K. Kato, M. Higashimori, and M. Kaneko, “Particle Swarm Optimization for Jump Height Maximization of a Serial Link Robot,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.8, pp. 956-963, 2007.
Data files:
References
  1. [1] M. Higashimori, M. Harada, I. Ishii, and M. Kaneko, “Jumping pattern generation for a serial link robot,” Journal of the Robotics Society of Japan, Vol.23, No.8, pp. 84-92, 2005.
  2. [2] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. IEEE Int. Conf. Neural Networks, Piscataway, NJ, pp. 1942-1948, 1995.
  3. [3] J. Kennedy and W. M. Spears, “Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator,” Proc. of IEEE Int. Conf. Evolutionary Computation, Anchorage, Alaska, 1998.
  4. [4] S. Koziel and Z. Michalewicz, “Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,” Evolutionary Computation, Vol.7, No.1, pp. 19-44, 1999.
  5. [5] T. Matsui, K. Kato, M. Sakawa, T. Uno, and K. Morihara, “Particle swarm optimization based heuristics for nonlinear programming problems,” Proc. of Int. MultiConference of Engineers and Computer Scientists 2007, pp. 1514-1518, 2007.
  6. [6] T. Mita, “Introduction to Nonlinear Control,” Shoko-do, Tokyo, 2000.
  7. [7] R. E. Paley, “A remarkable set of orthogonal functions,” Proc. London Math. Soc., 34, pp. 241-138, 1932.
  8. [8] K. E. Parsopoulos and M. N. Varahatis, “Recent approaches to global optimization problems through Particle Swarm Optimization,” Natural Computing, No.1, pp. 235-306, 2002.
  9. [9] Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” Proc. of IEEE Int. Conf. Evolutionary Computation, Anchorage, Alaska, 1998.

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