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

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

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