Skip to main content
Log in

Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming

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

Abstract

There has been growing interest in motion planning problems for mobile robots. In this field, the main research is to generate a motion for a specific robot and task without previously acquired motions. However it is too wasteful not to use hard-earned acquired motions for other tasks. Here, we focus on a mechanism of reusing acquired motion knowledge and study a motion planning system able to generate and reuse motion knowledge. In this paper, we adopt a tree-based representation for expressing knowledge of motion, and propose a hierarchical knowledge for realizing a reuse mechanism. We construct a motion planning system using hierarchical knowledge as motion knowledge and using genetic programming as a learning method. We apply a proposed method for the gait generation task of a six-legged locomotion robot and show its availability with computer simulation.

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

Access this article

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. Chang, H.: A new technique to handle local minimum for imperfect potential field-based motion planning, in: Proc. of 1996 IEEE Internat. Conf. on Robotics and Automation, 1996, pp. 108–112.

  2. Craig, J. J.: Introduction to Robotics, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  3. David, J.: Montana, Strongly typed genetic programming, BBN Technical Report #7866, 1994.

  4. Desai, J. P. and Kumar, V.: Nonholonomic motion planning for multiple mobile manipulators, in: Proc. of 1997 IEEE Internat. Conf. on Robotics and Automation, 1997, pp. 3409–3414.

  5. Fikes, R. E., Hart, P. E., and Nilsson, N. J.: Learning and executing generalized robot plans, Artificial Intell. 3(4) (1972), 251–288.

    Google Scholar 

  6. Flash, T. and Hogan, N.: The coordination of arm movements, J. Neurosci. 5 (1985), 1688–1703.

    Google Scholar 

  7. Fujisawa, Y., Hoshino, H., Fukuda, T., Kosuge, K., Muro, E., and Kikuchi, K.: Omnidirectional walking mechanism (1st Report, Control of moving with coordination of actuators), Trans. Japan Soc. Mech. Engrg. C 60(571) (1994), 964–969.

    Google Scholar 

  8. Fukuda, T., Adachi, Y., Hoshino, H., Kosuge, K., Muro, E., Matunaga, I., and Arai, F.: Omnidirectional walking mechanism (2nd Report, Inclination control while walking on rough terrain), Trans. Japan Soc. Mech. Engrg. C 61(589) (1995), 3620–3626.

    Google Scholar 

  9. Fukuda, T., Adachi, Y., Hoshino, H., and Muro, E.: Omnidirectional walking mechanism (3rd Report, Redundancy and trajectory control), Trans. Japan Soc. Mech. Engrg. C 63(607) (1997), 952–959.

    Google Scholar 

  10. Fukuda, T., Adachi, Y., Hoshino, H., Muro, E., and Kurashige, K.: Omnidirectional walking mechanism (redundancy and trajectory control), JSME Int. J. 40(4) (1997), 694–701.

    Google Scholar 

  11. Han, J., Chung, W. K., Youm, Y., and Kim, S. H.: Task-based design of modular robot manipulator using efficient genetic algorithm, in: Proc. of 1997 IEEE Internat. Conf. on Robotics and Automation, 1997, pp. 507–512.

  12. Jung, D. and Gupta, K. K.: Octree-based hierarchical distance maps for collision detection, in: Proc. of 1996 IEEE Internat. Conf. on Robotic and Automation, 1996, pp. 454–459.

  13. Koza, J.: Genetic Programming, on the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.

    Google Scholar 

  14. Latombe, J. C.: Robot Motion Planning, Kluwer Academic Publishers, 1991.

  15. Oomichi, T., Higuchi,M., and Ohnishi, K.: Design method of multifingered master manipulator with force and tactile feed-back free from operation restriction by mechanism, J. Robotics Soc. Japan 16(7) (1998), 942–950.

    Google Scholar 

  16. Saito, F. and Fukuda, T.: Reinforcement learning for motion control of real robots, JRSJ 13(1) (1995), 82–88.

    Google Scholar 

  17. Schwefel, H. P.: On the Evolution of Evolutionary Computation, IEEE Press, New York, 1994.

    Google Scholar 

  18. Shibata, T., Fukuda, T., Kosuge, K., and Arai, F.: Selfish and coordinative planning for multiple mobile robots by genetic algorithm, in: Proc. of the 31th IEEE Conf. on Decision and Control, Vol. 3, Tucson, 1992, pp. 497–503.

    Google Scholar 

  19. Tsubouchi, T. and Rude, M.: Motion planning for mobile robots in a time-varying environment: A survey, J. Robotics Mechatoronics 8(1) (1996), pp. 15–24.

    Google Scholar 

  20. Warren, C. W.: Fast path planning using modified A method, in: IEEE Internat. Conf. on Robotics and Automation, Vol. 2, 1993, pp. 662–667.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kurashige, K., Fukuda, T. & Hoshino, H. Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming. Journal of Intelligent and Robotic Systems 38, 121–134 (2003). https://doi.org/10.1023/A:1026204313001

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1026204313001

Navigation