Adaptive dynamic control of quadrupedal robotic gaits with Artificial Reaction Networks.
Gerrard, Claire E.
Coghill, George M.
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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.
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.