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Title: Adaptive dynamic control of quadrupedal robotic gaits with Artificial Reaction Networks.
Authors: Gerrard, Claire E.
McCall, John
Coghill, George M.
MacLeod, Christopher
Keywords: Artificial Neural Networks
Artificial Reaction Networks
Cellular intelligence
Biochemical networks
Issue Date: Nov-2012
Publisher: Springer
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: T. HUANG, Z. ZENG, C. LI and C. S. LEUNG, eds. Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part 1. Berlin: Springer, pp. 280-287.
Series/Report no.: Lecture Notes in Computer Science
LNCS 7663
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.
ISBN: 9783642344749
ISSN: 0302-9743
Appears in Collections:Conference publications (Computing)

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