Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Atılım Güneş Baydin 1
  • 1 Complex Adaptive Systems, Department of Applied Physics, Chalmers University of Technology, SE-412 96 Göteborg, Sweden

Abstract

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.

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  • [1] M. H. Raibert. Legged Robots That Balance. MIT Press, Boston, 1986.

  • [2] B. Adams, C. Breazeal, R. A. Brooks, and B. Scassellati. Humanoid robots: A new kind of tool. IEEE Intelligent Systems and Their Applications, 4:25–31, 2000.

  • [3] M. L. Swinson and D. J. Bruemmer. Expanding frontiers of humanoid robotics. IEEE Intelligent Systems and their Applications, 15(4):12–17, 2000.

  • [4] F. Yamaoka, T. Kanda, H. Ishiguro, and N. Hagita. Interacting with a human or a humanoid robot? In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, pages 2685–2691, 2007.

  • [5] Q. Huang, K. Li, and Y. Nakamura. Humanoid walk control with feedforward dynamic pattern and feedback sensory reflection. In K. Li, editor, Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, pages 29–34, 2001.

  • [6] S. H. Collins and A. Ruina. A bipedal walking robot with efficient and human-like gait. In A. Ruina, editor, Proceedings of the IEEE International Conference on Robotics and Automation ICRA, pages 1983–1988, 2005.

  • [7] K. Fujiwara, F. Kanehiro, S. Kajita, K. Yokoi, H. Saito, K. Harada, K. Kaneko, and H. Hirukawa. The first human-size humanoid that can fall over safely and stand up again. In F. Kanehiro, editor, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, volume 2, pages 1920–1926, 2003.

  • [8] J. Ruiz-del-Solar, R. Palma-Amestoy, R. Marchant, I. Parra-Tsunekawa, and P. Zegers. Learning to fall: Designing low damage fall sequences for humanoid soccer robots. Robotics and Autonomous Systems, 57(8):796–807, 2009.

  • [9] A. Sano and J. Furusho. 3D dynamic walking of biped robot by controlling the angular momentum. Journal of SICE, 26:459–466, 1990.

  • [10] S. Kajita, F. Kanehiro, K. Kaneko, K. Fujiwara, K. Harada, K. Yokoi, and H. Hirukawa. Biped walking pattern generation by using preview control of zero-moment point. In Proceedings of the IEEE International Conference on Robotics & Automation, Taipei, Taiwan, 2003, pages 1620–1626, 2003.

  • [11] G. Taga, Y. Yamaguchi, and H. Shimizu. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biological Cybernetics, 65:147–159, 1991.

  • [12] P. W. Latham. A simulation study of bipedal walking robots: modeling, walking algorithms, and neural network control. PhD thesis, University of New Hampshire, Durham, NH, 1992.

  • [13] T. Geng, B. Porr, and F. Wörgötter. Fast biped walking with a sensor-driven neuronal controller and real-time online learning. The International Journal of Robotics Research, 25(3):243–259, 2006.

  • [14] A. J. ljspeert. Central pattern generators for locomotion control in animals and robots: A review. Neural Networks, 21:642–653, 2008.

  • [15] S. Rossignol, R. Dubuc, and J. Gossard. Dynamic sensorimotor interactions in locomotion. Physiological Reviews, 86:89–154, 2006.

  • [16] L. Righetti and A. ljspeert. Programmable central pattern generators: an application to biped locomotion control. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pages 1585–1590, 2006.

  • [17] G. L. Liu, M. K. Habib, and K. Watanabe. Central pattern generators based on matsuoka oscillators for the locomotion of biped robots. Artificial Life and Robotics, 12:264–269, 2008.

  • [18] M. A. Lewis, F. Tenore, and R. Etienne-Cummings. CPG design using inhibitory networks. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’05), Barcelona, Spain, 2005.

  • [19] J. Pratt, C. Chew, A. Torres, P. Dilworth, and G. Pratt. Virtual model control: An intuitive approach for bipedal locomotion. The International Journal of Robotics Research, 20(2):129–143, 2001.

  • [20] M. A. Lewis, R. Etienne-Cummings, M. J. Haartmann, Z. R. Xu, and A. H. Cohen. An in silico central pattern generator: Silicon oscillator, coupling, entrainment, and physical computation. Biological Cybernetics, 88:137–151, 2003.

  • [21] F. Delcomyn. Neural basis or rhythmic behavior in animals. Science, 210:492–498, 1980.

  • [22] E. Marder and D. Bucher. Central pattern generators and the control of rhythmic movements. Current Biology, 11(23):R986–R996, 2001.

  • [23] T. Komatsu and M. Usui. Dynamic walking and running of a bipedal robot using hybrid central pattern generator method. In Proceedings of the IEEE International Conference on Mechatronics and Automation, pages 987–992, 2005.

  • [24] R. Heliot and B. Espiau. Multisensor input for CPG-based sensory–motor coordination. IEEE Transactions on Robotics, 24(1):191–195, 2008.

  • [25] S. Aoi and K. Tsuchiya. Locomotion control of a biped robot using nonlinear oscillators. Autonomous Robots, 19:219–232, 2005.

  • [26] J. Conradt and P. Varshavskaya. Distributed central pattern generator control for a serpentine robot. In O. Kaynak, E. Alpaydin, E. Oja, and L. Xu, editors, Proceedings of the International Conference on Artificial Neural Networks, 2003.

  • [27] D. Lachat, A. Crespi, and A. J. ljspeert. Boxybot: a swimming and crawling fish robot controlled by a central pattern generator. In A. Crespi, editor, Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechanics (BioRob), pages 643–648, 2006.

  • [28] K. Matsuoka. Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics, 52:367–376, 1985.

  • [29] K. Matsuoka. Mechanisms of frequency and pattern control in the neural rhythm generators. Biological Cybernetics, 56:345–353, 1987.

  • [30] S. H. Collins, M. Wisse, and A. Ruina. A three-dimensional passive dynamic walking robot with two legs and knees. International Journal of Robotics Research, 20(7):607–615, 2001.

  • [31] R. Pfeifer and G. Gómez. Morphological computation: connecting brain, body, and environment. In Creating Brain-Like Intelligence. Springer Verlag, 2009.

  • [32] C. Chevallereau, G. Abba, Y. Aoustin, F. Plestan, C. Westervelt, C. Canudas-De-Wit, and J. Grizzle. RABBIT: A testbed for advanced control theory. IEEE Control Systems Magazine, 23(5):57–79, 2003.

  • [33] M. H. Raibert. Hopping in legged systems—modeling and simulation for the 2D one-legged case. IEEE Transactions on Systems, Man and Cybernetics, 14:451–463, 1984.

  • [34] M. Wineberg and F. Oppacher. The benefits of computing with introns. In Proceedings of the First Annual Conference on Genetic Programming, pages 410–415. MIT Press, 1996.

  • [35] J. Pratt and G. Pratt. Intuitive control of a planar bipedal walking robot. In Proceedings of the IEEE International Conference of Robotics and Automation, pages 2014–2021, Leuven, Belgium, 1998.

  • [36] J. A. Ellis, M. Stebbing, and S. B. Harrap. Significant population variation in adult male height associated with the Y chromosome and the aromatase gene. Clinical Endocrinology and Metabolism, 86(9):4147–4150, 2001.

  • [37] N. Carey. Establishing pedestrian walking speeds. Technical report, Portland State University, 2005.

  • [38] R. J. Peterka and P. J. Loughlin. Dynamic regulation of sensorimotor integration in human postural control. Journal of Neurophysiology, 91:410–423, 2004.

  • [39] A. Prochazka, D. Gillard, and D. J. Bennett. Implications of positive feedback in the control of movement. Journal of Neurophysiology, 77:3237–3251, 1997.

  • [40] A. Prochazka and S. Yakovenko. Predictive and reactive tuning of the locomotor CPG. Integrative and Comparative Biology, 47(4):474–481, 2007.

  • [41] J. C. Bongard and R. Pfeifer. Evolving complete agents using artificial ontogeny. In F. Hara and R. Pfeifer, editors, Morphofunctional Machines: The New Species, pages 237–258. Springer-Verlag, 2003.

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Paladyn. Journal of Behavioral Robotics is a fully peer-reviewed, open access journal that publishes original, high-quality research works and review articles on topics broadly related to neuronally and psychologically inspired robots and other behaving autonomous systems. The journal is indexed in SCOPUS.

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