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A General Rhythmic Pattern Generation Architecture for Legged Locomotion

A General Rhythmic Pattern Generation Architecture for Legged Locomotion

Zhijun Yang, Felipe M.G. França
Copyright: © 2009 |Pages: 29
ISBN13: 9781599049960|ISBN10: 1599049961|ISBN13 Softcover: 9781616925376|EISBN13: 9781599049977
DOI: 10.4018/978-1-59904-996-0.ch012
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MLA

Yang, Zhijun, and Felipe M.G. França. "A General Rhythmic Pattern Generation Architecture for Legged Locomotion." Advancing Artificial Intelligence through Biological Process Applications, edited by Ana B. Porto Pazos, et al., IGI Global, 2009, pp. 202-230. https://doi.org/10.4018/978-1-59904-996-0.ch012

APA

Yang, Z. & França, F. M. (2009). A General Rhythmic Pattern Generation Architecture for Legged Locomotion. In A. Porto Pazos, A. Pazos Sierra, & W. Buño Buceta (Eds.), Advancing Artificial Intelligence through Biological Process Applications (pp. 202-230). IGI Global. https://doi.org/10.4018/978-1-59904-996-0.ch012

Chicago

Yang, Zhijun, and Felipe M.G. França. "A General Rhythmic Pattern Generation Architecture for Legged Locomotion." In Advancing Artificial Intelligence through Biological Process Applications, edited by Ana B. Porto Pazos, Alejandro Pazos Sierra, and Washington Buño Buceta, 202-230. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-996-0.ch012

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Abstract

As an engine of almost all life phenomena, the motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. After a brief review of some recent research results on locomotor central pattern generators (CPG), which is a concrete branch of studies on the CNS generating rhythmic patterns, this chapter presents a novel, macroscopic and model-independent approach to the retrieval of different patterns of coupled neural oscillations observed in biological CPGs during the control of legged locomotion. Based on scheduling by multiple edge reversal (SMER), a simple and discrete distributed synchroniser, various types of oscillatory building blocks (OBB) can be reconfigured for the production of complicated rhythmic patterns and a methodology is provided for the construction of a target artificial CPG architecture behaving as a SMER-like asymmetric Hopfield neural networks.

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