Hostname: page-component-74656f9884-2dmjx Total loading time: 0 Render date: 2026-11-09T21:31:52.247Z Has data issue: false hasContentIssue false
  • English
  • Français

Coupling effect analysis between the central nervous system and the CPG network with proprioception

Published online by Cambridge University Press:  01 April 2014

Bin He ,
Qiang Lu  and
Zhipeng Wang
Bin He*
Affiliation:
College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China
Qiang Lu
Affiliation:
College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China College of Information and Engineering, Taishan Medical University, Taian, 271016, China
Zhipeng Wang
Affiliation:
College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China
*
*Corresponding author. E-mail: hebin@tongji.edu.cn
Rights & Permissions [Opens in a new window]

Summary

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Human rhythmic movement is generated by central pattern generators (CPGs), and their application to robot control has attracted interest of many scientists. But the coupling relationship between the central nervous system and the CPG network with external inputs is still not unveiled. According to biological experiment results, the CPG network is controlled by the neural system; in other words, the interaction between the central nervous system and the CPG network can control human movement effectively. This paper offers a complex human locomotion model, which illustrates the coupling relationship between the central nervous system and the CPG network with proprioception. Based on Matsuoka's CPG model (K. Matsuoka, Biol. Cybern. 52(6), 367–376 (1985)), the stability and robustness of the CPG network are analyzed with external inputs. In order to simulate the coupling relationship, the Radial Basis Function (RBF) neural network is used to simulate the cerebral cortex, and the Credit-Assignment Cerebellar Model Articulation Controller algorithm is employed to realize the locomotion mode conversion. A seven-link biped robot is chosen to simulate the walking gait. The main discoveries include: (1) the output of a new CPG network, which is stable and robust, can be treated as proprioception. Proprioception provides the central nervous system with the information about all joint angles; (2) analysis on a new locomotion model reveals that the cerebral cortex can modulate CPG parameters, leading to adjustment in walking gait.

Keywords

Central pattern generator (CPG)ProprioceptionCentral nervous systemCA-CMACHuman locomotion

Information

Type
Articles
Information
Robotica , Volume 33 , Issue 6 , July 2015 , pp. 1281 - 1294
Copyright
Copyright © Cambridge University Press 2014 

References

1. Matsuoka, K., “Sustained oscillations generated by mutually inhibiting neurons with adaptation,” Biol. Cybern. 52 (6), 367–376 (1985).Google Scholar
2. Fu, C., Tan, F. and Chen, K., “A simple walking strategy for biped walking based on an intermittent sinusoidal oscillator,” Robotica 28 (6), 869–884 (2010).Google Scholar
3. Liu, G., Habib, M. K., Watanabe, K. and Izumi, K., “Central pattern generators based on Matsuoka oscillators for the locomotion of biped robots,” Artif. Life Robot. 12, 264–269 (2008).Google Scholar
4. Ha, T. and Choi, C. H., “An effective trajectory generation method for bipedal walking,” Robot. Auton. Syst. 55, 795–810 (2007).Google Scholar
5. Zhang, X. and Zheng, H., “Autonomously clearing obstacles using the biological flexor reflex in a quadrupedal robot,” Robotica 26, 1–7 (2008).Google Scholar
6. Kondo, T. and Ito, K., “Periodic Motion Control by Modulating CPG Parameters Based on Time-Serials Recognition,” Proceedings of the 8th European Conference on Artificial Life, Canterbury, UK (2005) pp. 906–915.Google Scholar
7. Ehrsson, H. H., Spence, C. and Passingham, R. E., “‘That's my hand!’ Activity in the premotor cortex reflects feeling of ownership of a limb,” Science 305, 875–877 (2004).Google Scholar
8. Ehrsson, H. H., “The experimental induction of out-of-body experiences,” Science 317, 1048 (2007).Google Scholar
9. Overholt, J. L., Hudas, G. R. and Gerhart, G. R., “Defining Proprioceptive Behaviors for Autonomous Mobile Robots,” Proceedings of SPIE, Orlando, FL (2002) pp. 287–294.Google Scholar
10. van Beers, R. J., Sittig, A. C. and van der Gon, J. J. D., “The precision of proprioceptive position sense,” Exp. Brain Res. 122, 367–377 (1998).Google Scholar
11. Bartsch, S. and Kirchner, F., “Robust Control of a Humanoid Robot Using a Bio-Inspired Approach Based on Central Pattern Generators, Reflexes, and Proprioceptive Feedback,” IEEE International Conference on Robotics and Biomimetics, Kunming, China (2006) pp. 1547–1552.Google Scholar
12. Drew, T., Prentice, S. and Schepens, B., “Cortical and brainstem control of locomotion,” Prog. Brain Res. 143, 251–261 (2004).Google Scholar
13. Takakusaki, K. and Okumura, T., “Neurobiological basis of controlling posture and locomotion,” Adv. Robot. 22, 1629–1663 (2008).Google Scholar
14. Taga, G., “A model of the neuro-musculo-skeletal system for human locomotion. I. Emergence of basic gait,” Biol. Cybern. 73, 97–111 (1995).Google Scholar
15. Huang, Q., Yokoi, K. and Kajita, S., “Planning walking patterns for a biped robot,” IEEE Trans. Robot. Autom. 17 (3), 280–289 (2001).Google Scholar
16. Fukuoka, Y., Kimura, H. and Cohen, A. H., “Adaptive dynamic walking of a quadruped robot on irregular terrain based on biological concepts,” Int. J. Robot. Res. 22, 187–203 (2003).Google Scholar
17. Zhang, X., Biological-Inspired Rhythmic Motion & Environmental Adaptability for Quadruped Robot, PhD Dissertation, Department Of Mechanical Engineering, Tsinghua University, Beijing, China (2004) pp. 41–62.Google Scholar
18. Kim, J. J., Lee, J. W. and Lee, J. J., “Central pattern generator parameter search for a biped walking robot using nonparametric estimation based particles swarm optimization,” Int. J. Control Autom. Syst. 7 (3), 447–457 (2009).Google Scholar
19. Kim, Y., Tagawa, Y., Obinata, G. and Hase, K., “Robust control of CPG-based 3D neuromusculoskeletal walking model,” Biol. Cybern. 105, 269–282 (2011).Google Scholar
20. Wong, W. E., Debroy, V., Golden, R., Xu, X. and Thuraisingham, B., “Effective software fault localization using an RBF neural network,” IEEE Trans. Reliab. 61 (1), 149–169 (2012).Google Scholar
21. Wu, X., Jiang, G., Wang, X., Fang, N., Zhao, L., Ma, Y. and Luo, S., “Prediction of reservoir sensitivity using RBF neural network with trainable radial basis function,” Neural Comput. Appl. 22, 947–953 (2013).Google Scholar
22. Su, S. F., Tao, T. and Hung, T. H., “Credit-assigned CMAC and its application to online learning robust controllers,” IEEE Trans. Syst. Man Cybern. B 33 (2), 202–213 (2003).Google Scholar
23. Wang, Y., Tao, Y., Nie, B., and Liu, H., “Optimal design of control for scan tracking measurement: A CMAC approach,” Measurement 46, 384–392 (2013).Google Scholar
24. Matsuoka, K., “Analysis of a neural oscillator,” Biol. Cybern. 104, 297–304 (2011).Google Scholar
25. Liu, C., Atkeson, C. G. and Su, J., “Biped walking control using a trajectory library,” Robotica 31 (2), 311–322 (2013).Google Scholar
26. Bessonnet, G., Chesse, S. and Sardain, P., “Optimal gait synthesis of a seven-link planar biped,” Int. J. Robot. Res. 23 (10–11), 1059–1073 (2004).Google Scholar
27. Ishida, M., Kato, S., Kanoh, M. and Itoh, H., “Generating Locomotion for Biped Robots Based on the Dynamic Passivization of Joint Control,” Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX (2009) pp. 3157–3162.Google Scholar