Multiple Decoupled CPGs with Local Sensory Feedback for Adaptive Locomotion Behaviors of Bio-inspired Walking Robots

  • Subhi Shaker Barikhan
  • Florentin Wörgötter
  • Poramate Manoonpong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8575)

Abstract

Walking animals show versatile locomotion. They can also adapt their movement according to the changes of their morphology and the environmental conditions. These emergent properties are realized by biomechanics, distributed central pattern generators (CPGs), local sensory feedback, and their interactions during body and leg movements through the environment. Based on this concept, we present here an artificial bio-inspired walking system. Its intralimb coordination is formed by multiple decoupled CPGs while its interlimb coordination is attained by the interactions between body dynamics and the environment through local sensory feedback of each leg. Simulation results show that this bio-inspired approach generates self-organizing emergent locomotion allowing the robot to adaptively form regular patterns, to stably walk while pushing an object with its front legs or performing multiple stepping of the front legs, to deal with morphological change, and to synchronize its movement with another robot during a collaborative task.

Keywords

Adaptive behavior Hexapod locomotion Brain-body-environment interaction Autonomous robots Neural networks 

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References

  1. 1.
    Ambe, Y., Nachstedt, T., Manoonpong, P., Wörgötter, F., Aoi, S., Matsuno, F.: Stability analysis of a hexapod robot driven by distributed nonlinear oscillators with a phase modulation mechanism. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5087–5092 (2013)Google Scholar
  2. 2.
    Aoi, S., Yamashita, T., Tsuchiya, K.: Hysteresis in the gait transition of a quadruped investigated using simple body mechanical and oscillator network models. Physical Review E 83(6), 061909 (2011)Google Scholar
  3. 3.
    Campos, R., Matos, V., Santos, C.: Hexapod locomotion: A nonlinear dynamical systems approach. In: IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society, pp. 1546–1551 (2010)Google Scholar
  4. 4.
    Canavier, C.C., Butera, R.J., Dror, R.O., Baxter, D.A., Clark, J.W., Byrne, J.H.: Phase response characteristics of model neurons determine which patterns are expressed in a ring circuit model of gait generation. Biological Cybernetics 77(6), 367–380 (1997)CrossRefMATHGoogle Scholar
  5. 5.
    Dickinson, M.H., Farley, C.T., Full, R.J., Koehl, M.A.R., Kram, R., Lehman, S.: How animals move: An integrative view. Science 288(5463), 100–106 (2000)CrossRefGoogle Scholar
  6. 6.
    Fujiki, S., Aoi, S., Kohda, T., Senda, K., Tsuchiya, K.: Emergence of hysteresis in gait transition of a hexapod robot driven by nonlinear oscillators with phase resetting. In: 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1638–1643 (2012)Google Scholar
  7. 7.
    Grabowska, M., Godlewska, E., Schmidt, J., Daun-Gruhn, S.: Quadrupedal gaits in hexapod animals-inter-leg coordination in free-walking adult stick insects. The Journal of Experimental Biology 215(24), 4255–4266 (2012)CrossRefGoogle Scholar
  8. 8.
    Graham, D.: The effect of amputation and leg restraint on the free walking coordination of the stick insectCarausius morosus. Journal of Comparative Physiology 116(1), 91–116 (1977)CrossRefGoogle Scholar
  9. 9.
    Hesse, F., Martius, G., Manoonpong, P., Biehl, M., Wörgötter, F.: Modular Robot Control Environment Testing Neural Control on Simulated and Real Robots. In: Frontiers in Computational Neuroscience, Conference Abstract: Bernstein Conference (2012), doi:10.3389/conf.fncom.2012.55.00179Google Scholar
  10. 10.
    Ijspeert, A.J., Crespi, A., Ryczko, D., Cabelguen, J.M.: From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817), 1416–1420 (2007)CrossRefGoogle Scholar
  11. 11.
    Manoonpong, P., Parlitz, U., Wörgötter, F.: Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines. Frontiers in Neural Circuits 7, 12 (2013)CrossRefGoogle Scholar
  12. 12.
    Owaki, D., Kano, T., Nagasawa, K., Tero, A., Ishiguro, A.: Simple robot suggests physical interlimb communication is essential for quadruped walking. Journal of the Royal Society Interface 10(78) (2013)Google Scholar
  13. 13.
    Shim, Y., Husbands, P.: Chaotic exploration and learning of locomotion behaviors. Neural Computation 24(8), 2185–2222 (2012)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Zill, S., Schmitz, J., Büschges, A.: Load Sensing and Control of Posture and Locomotion. Arthropod Structure & Development 33, 273–286 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Subhi Shaker Barikhan
    • 1
    • 2
  • Florentin Wörgötter
    • 1
  • Poramate Manoonpong
    • 1
    • 3
  1. 1.Bernstein Center for Computational Neuroscience (BCCN), The Third Institute of PhysicsUniversity of GöttingenGöttingenGermany
  2. 2.Institute of Computer ScienceUniversity of GöttingenGöttingenGermany
  3. 3.Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkOdense MDenmark

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