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Computational Intelligence and Neuroscience
Volume 2016, Article ID 5615618, 13 pages
http://dx.doi.org/10.1155/2016/5615618
Research Article

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation

1Division of Postgraduate Studies and Research, Leon Institute of Technology, 37290 Leon, GTO, Mexico
2Department of Electronics, DICIS, University of Guanajuato, 36885 Salamanca, GTO, Mexico
3Department of Organizational Studies, DCEA, University of Guanajuato, 36250 Guanajuato, GTO, Mexico
4Division of Postgraduate Studies and Research, Tijuana Institute of Technology, 22414 Tijuana, BC, Mexico

Received 12 February 2016; Revised 6 April 2016; Accepted 24 May 2016

Academic Editor: Ricardo Aler

Copyright © 2016 A. Espinal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Nebojsa Bozanic, Irene Malvestio, Fleur Zeldenrust, Eero Satuvuori, Mario Mulansky, Kerstin Lenk, and Thomas Kreuz, “Measures of spike train synchrony for data with multiple time scales,” Journal of Neuroscience Methods, vol. 287, pp. 25–38, 2017. View at Publisher · View at Google Scholar
  • Andres Espinal, Angel F. Jimenez-Fernandez, Francisco Gomez-Rodriguez, Alejandro Linares-Barranco, Brayan Cuevas-Arteaga, Juan Pedro Dominguez-Morales, and Horacio Rostro-Gonzalez, “A SpiNNaker Application: Design, Implementation and Validation of SCPGs,” Advances in Computational Intelligence, vol. 10305, pp. 548–559, 2017. View at Publisher · View at Google Scholar
  • Erick I. Guerra-Hernandez, Andres Espinal, Patricia Batres-Mendoza, Carlos Garcia-Capulin, Rene Romero-Troncoso, and Horacio Rostro-Gonzalez, “A FPGA-based neuromorphic locomotion system for multi-legged robots,” IEEE Access, pp. 1–1, 2017. View at Publisher · View at Google Scholar
  • Jianjun Ni, Liuying Wu, Pengfei Shi, and Simon X. Yang, “A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–16, 2017. View at Publisher · View at Google Scholar
  • Manuel Ornelas-Rodríguez, Horacio Rostro-Gonzalez, Andrés Espinal, Marco Sotelo-Figueroa, and Héctor J. Estrada-García, “Spiking Central Pattern Generators through Reverse Engineering of Locomotion Patterns,” Cognitive and Computational Neuroscience - Principles, Algorithms and Applications, 2018. View at Publisher · View at Google Scholar
  • Matteo Lodi, Andrey Shilnikov, and Marco Storace, “Design of Synthetic Central Pattern Generators Producing Desired Quadruped Gaits,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 3, pp. 1028–1039, 2018. View at Publisher · View at Google Scholar
  • Eero Satuvuori, Irene Malvestio, and Thomas Kreuz, “Measures of Spike Train Synchrony and Directionality,” Mathematical and Theoretical Neuroscience, vol. 24, pp. 201–222, 2018. View at Publisher · View at Google Scholar
  • Yasuhiko Jimbo, Christiane Thielemann, Manuel Ciba, Takuya Isomura, and Andreas Bahmer, “Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony,” Journal of Neuroscience Methods, vol. 293, pp. 136–143, 2018. View at Publisher · View at Google Scholar
  • Chenghong Zhang, Bin He, An Ding, Shoulin Xu, Zhipeng Wang, and Yanmin Zhou, “Motion Simulation of Ionic Liquid Gel Soft Actuators Based on CPG Control,” Computational Intelligence and Neuroscience, vol. 2019, pp. 1–11, 2019. View at Publisher · View at Google Scholar