Yang, Z.; Rocha, M.; Lima, P.; Karamanoglu, M.; Franca, F.

A legged central pattern generation model for autonomous gait transition

Conference paper

Yang, Z., Rocha, M., Lima, P., Karamanoglu, M. and Franca, F. 2014. A legged central pattern generation model for autonomous gait transition. 2014 IEEE International Joint Conference on Neural Networks (IJCNN). Beijing, China 06 - 11 Jul 2014 IEEE. pp. 1992-1995 https://doi.org/10.1109/IJCNN.2014.6889779

ISBN
Type	Conference paper
Title	A legged central pattern generation model for autonomous gait transition
Authors	Yang, Z., Rocha, M., Lima, P., Karamanoglu, M. and Franca, F.
Abstract	In this work, a generalized central pattern generator (CPG) model is formulated to generate a full range of gait patterns for a hexapod insect. To this end, a recurrent neural network module, as the building block for rhythmic patterns, is proposed to extend the concept of oscillatory building blocks (OBB) for constructing a CPG model. The model is able to make transitions between different gait patterns by simply adjusting one model parameter. Simulation results are further presented to show the effectiveness and performance of the CPG network.
Keywords	Neurons; Mathematical model; Oscillators; Joints ; Biological system modeling; Legged locomotion; Generators
Conference	2014 IEEE International Joint Conference on Neural Networks (IJCNN)
Page range	1992-1995
Proceedings Title	2014 International Joint Conference on Neural Networks (IJCNN)
Series	IEEE International Joint Conference on Neural Networks (IJCNN)
ISSN	2161-4393
Electronic	2161-4407
Electronic	9781479914845
Publisher	IEEE
Publication dates
Print	31 Jul 2014
Online	04 Sep 2014
Publication process dates
Deposited	28 May 2015
Completed	Jul 2014
Output status	Published
Publisher's version	File Access Level Restricted
Copyright Statement	Access to full text restricted pending copyright check.
Web address (URL)	http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889779
Digital Object Identifier (DOI)	https://doi.org/10.1109/IJCNN.2014.6889779
Web of Science identifier	WOS:000371465702013
Language	English

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