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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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2081-4836
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Evolution of central pattern generators for the control of a five-link bipedal walking mechanism

Atılım Güneş Baydin
  • Complex Adaptive Systems, Department of Applied Physics, Chalmers University of Technology, SE-412 96 Göteborg, Sweden
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Published Online: 2012-05-12 | DOI: https://doi.org/10.2478/s13230-012-0019-y

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.

Keywords: central pattern generator; humanoid robotics; evolutionary algorithms; evolutionary robotics; bipedal walking

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About the article

Received: 2011-10-12

Accepted: 2011-12-30

Published Online: 2012-05-12

Published in Print: 2012-03-01


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 3, Issue 1, Pages 45–53, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-012-0019-y.

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© Atılım Güneş Baydin. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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