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A Bio-Inspired Robotic Mechanism for Autonomous Locomotion in Unconventional Environments

  • Darío Maravall
  • Javier de Lope
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 116)

Abstract

This chapter presents a robotic mechanism aimed at navigating in unconventional environments like rigid aerial lines — power, telephone, railroad — and reticulated structures — ladders, grills, bars, etc —. A novel method of obstacle avoidance for this mechanism is also introduced. The computation of collision-free trajectories generally requires the analytical description of the physical structure of the environment and the solution of the kinematic equations. For dynamic, uncertain environments with unknown obstacles, however, it is very hard to get realtime collision avoidance by means of analytical techniques. The main strength of the proposed method resides, precisely, in that it departs from the analytical approach, as it does not use formal descriptions of the location and shape of the obstacles, nor does it solve the kinematic equations of the mechanism. Instead, the method follows the perception-reason-action paradigm and is based on a reinforcement learning process guided by perceptual feedback, which can be considered as biologically inspired at the functional level. From this perspective, obstacle avoidance is modeled as a multi-objective optimization problem. The method, as shown in the chapter, can be straightforwardly applied to real-time collision avoidance for articulated mechanisms, including conventional manipulator arms.

Keywords

Performance Index Reinforcement Learning Performance Function Collision Avoidance Obstacle Avoidance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Saito, F., Fukuda, T., Arai, F. (1994) Swing and locomotion control for a twolink brachiation robot. IEEE Control Syst. Mag, 14, 5–12CrossRefGoogle Scholar
  2. 2.
    Nakanishi, J., Fukuda, T., Koditschek, D.E. (2000) A brachiating robot controller. IEEE Trans. on Robotics and Automation, 16(2), 109–123CrossRefGoogle Scholar
  3. 3.
    Maravall, D., Baumela, L. (1996) Robotic systems with perceptual feedback and anticipatory behavior. In R. Moreno-Diaz, J. Mira-Mira (eds.), Brain Processes, Theories and Models. MIT Press, Cambridge, Massachusetts, 532–540Google Scholar
  4. 4.
    Albus, J.S. (1975) A new approach to manipulator control: The cerebellar model articulation controller (CMAC). ASME J. of Dynamics Systems, Meas., & Control, 97, 220–227MATHCrossRefGoogle Scholar
  5. 5.
    Meystel, A.M., Albus, J.S. (2002) Intelligent Systems: Architecture, Design and Control. John Wiley & Sons, New YorkGoogle Scholar
  6. 6.
    Kawato, M. Cerebellum and motor control. (1995) In M.A. Arbib (ed.), The Handbook of Brain Theory and Neural Netwoks. MIT Press, Cambridge, Massachusetts, 172–178Google Scholar
  7. 7.
    Franklin, S. (1995) Artificial Minds. MIT Press, Cambridge, Massachusetts.Google Scholar
  8. 8.
    Mel, B.W. (1990) Connectionist Robot Motion Planning. Academic Press, BostonMATHGoogle Scholar
  9. 9.
    Werbos, P.J. (1990) A menu of designs for reinforcement learning over time. In W.T. Miller III, R.S. Sutton, P.J. Werbos (eds.), Neural Networks for Control. MIT Press, Cambridge, Massachusetts, 67–95Google Scholar
  10. 10.
    Bryson, A.E., Ho, Y.C. (1969) Applied Optimal Control: Optimization, Estimation and Control. Hemisphere, MassachusettsGoogle Scholar
  11. 11.
    Westphal, L.C. (1995) Sourcebook of Control Systems Engineering. Chapman & Hall, LondonCrossRefGoogle Scholar
  12. 12.
    Jang, J.-R.R., Sun, C.-T., Mizutani, E. (1997) Neuro-Fuzzy and Softcomputing. Prentice-Hall, Upper Saddle River, New JerseyGoogle Scholar
  13. 13.
    Lu, Y.-Z. (1997) Industrial Intelligent Control. John Wiley & Sons, New YorkGoogle Scholar
  14. 14.
    Sutton, R.S., Barto, A.G. (1998) Reinforcement Learning, MIT Press, Cambridge, MassachusettsGoogle Scholar
  15. 15.
    Zhou, C. (2000) Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot. Soft Computing, 4, 238–250MATHCrossRefGoogle Scholar
  16. 16.
    Zhou, C., Yang, Y., Jia, X. (2001) Incorporating perception based information in reinforcement learning using computing with words. In J. Mira, A. Prieto (eds.), Bio-Inspired Applications of Connectionism, LNCS 2085, Springer Verlag, Berlin, 476–483CrossRefGoogle Scholar
  17. 17.
    Barto, A.G. (1995) Reinforcement learning in motor control. In M.A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, Massachusetts, 809–813Google Scholar
  18. 18.
    Jacob, C. (1999) Stochastic search methods. In M. Berthold, D.J. Hand (eds.), Intelligent Data Analysis, Springer-Verlag, Berlin, 299–350Google Scholar
  19. 19.
    White, D.A., Sofge, D.A. (1992) Handbook of Intelligent Control, Van Nostrand Reinhold, New YorkGoogle Scholar
  20. 20.
    Chankong, V., Haimes, Y.Y. (1987) Multiple objective optimization: Pareto Optimality. In M.G. Singh (ed.), Systems & Control Encyclopedia, Vol. 5, Pergamon Press, Oxford, 3156–3165Google Scholar
  21. 21.
    Kang, D.-O. et al. (2001) Multiobjective navigation of a guide mobile robot for the visually impaired based on intention inference of obstacles. Autonomous Robots, 10, 213–230MATHCrossRefGoogle Scholar
  22. 22.
    Maravall, D., De Lope, J. (2002) A reinforcement learning method for dynamic obstacle avoidance in robotic mechanisms. 5th International Conference on Computational Intelligence Systems for Applied Research, Gent, Belgium, September 16–18, 2002 (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Darío Maravall
    • 1
  • Javier de Lope
    • 1
  1. 1.Department of Artificial Intelligence Faculty of Computer ScienceUniversidad Politécnica de Madrid Campus de MontegancedoMadridSpain

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