Prescription of rhythmic patterns for legged locomotion

Yang, Zhijun ORCID: https://orcid.org/0000-0003-2615-4297, Zhang, Daqiang, Rocha, Marlon V., Lima, Priscila M. V., Karamanoglu, Mehmet ORCID: https://orcid.org/0000-0002-5049-2993 and Franca, Felipe M. G. (2017) Prescription of rhythmic patterns for legged locomotion. Neural Computing and Applications, 28 (11). pp. 3587-3601. ISSN 0941-0643 (doi:https://doi.org/10.1007/s00521-016-2237-4)

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Abstract

As the engine behind many life phenomena, motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. In this work, a novel, macroscopic and model-independent approach is presented for creating different patterns of coupled neural oscillations observed in biological central pattern generators (CPG) during the control of legged locomotion. Based on a simple distributed state machine, which consists of two nodes sharing pre-defined number of resources, the concept of oscillatory building blocks (OBBs) is summarised for the production of elaborated rhythmic patterns. Various types of OBBs can be designed to construct a motion joint of one degree-of-freedom (DOF) with adjustable oscillatory frequencies and duty cycles. An OBBs network can thus be potentially built to generate a full range of locomotion patterns of a legged animal with controlled transitions between different rhythmic patterns. It is shown that gait pattern transition can be achieved by simply changing a single parameter of an OBB module. Essentially this simple mechanism allows for the consolidation of a methodology for the construction of artificial CPG architectures behaving as an asymmetric Hopfield neural network. Moreover, the proposed CPG model introduced here is amenable to analogue and/or digital circuit integration.

Item Type: Article
Additional Information: First online: 16 March 2016
Keywords (uncontrolled): Central Pattern Generator; Oscillatory Building Blocks; Legged Locomotion; Parallel Processing Systems
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19156
Notes on copyright: This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at Springer via http://dx.doi.org/10.1007/s00521-016-2237-4
Useful Links:
Depositing User: Zhijun Yang
Date Deposited: 08 Apr 2016 11:30
Last Modified: 03 Aug 2019 14:41
URI: https://eprints.mdx.ac.uk/id/eprint/19156

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