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Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks.

Gerrard, Claire E.; McCall, John; Coghill, George M.; Macleod, Christopher

Authors

Claire E. Gerrard

George M. Coghill

Christopher Macleod



Contributors

Tingwen Huang
Editor

Zhigang Zeng
Editor

Chuandong Li
Editor

Chi Sing Leung
Editor

Abstract

The Artificial Reaction Network (ARN) is a bio-inspired connectionist paradigm based on the emerging field of Cellular Intelligence. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, and S-Systems. In this paper, elements of temporal dynamics and pattern recognition are combined within a single ARN control system for a quadrupedal robot. The results show that the ARN has similar applicability to Artificial Neural Network models in robotic control tasks. In comparison to neural Central Pattern Generator models, the ARN can control gaits and offer reduced complexity. Furthermore, the results show that like spiky neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network.

Citation

GERRARD, C.E., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2012. Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. In Huang, T., Zeng, Z., Li, C. and Leung, C.S. (eds.) Proceedings of the 19th International conference on neural information processing (ICONIP 2012), 12-15 November 2012, Doha, Qatar. Lecture notes in computer science, 7663. Berlin: Springer [online], part I, pages 280-287. Available from: https://doi.org/10.1007/978-3-642-34475-6_34

Conference Name 19th International conference on neural information processing (ICONIP 2012)
Conference Location Doha, Qatar
Start Date Nov 12, 2012
End Date Nov 15, 2012
Acceptance Date Nov 30, 2012
Online Publication Date Nov 30, 2012
Publication Date Dec 31, 2012
Deposit Date Dec 4, 2012
Publicly Available Date Dec 4, 2012
Print ISSN 0302-9743
Publisher Springer
Pages 280-287
Series Title Lecture notes in computer science
Series Number 7663
Series ISSN 0302-9743
ISBN 9783642344749
DOI https://doi.org/10.1007/978-3-642-34475-6_34
Keywords Artificial neural networks; Artificial reaction networks; Cellular intelligence; Biochemical networks
Public URL http://hdl.handle.net/10059/778

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