Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks

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

doi:10.1007/978-3-642-34475-6_34

Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks

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

UHI North West and Hebrides

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages	280-287
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-34475-6_34
Publication status	Published - 2012
Event	19th International Conference on Neural Information Processing, ICONIP 2012 - Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7663 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Neural Information Processing, ICONIP 2012
Country/Territory	Qatar
City	Doha
Period	12/11/12 → 15/11/12

Keywords

Artificial Neural Networks
Artificial Reaction Networks
Biochemical Networks
Cellular Intelligence

Access to Document

10.1007/978-3-642-34475-6_34

Cite this

Gerrard, C. E., McCall, J., Coghill, G. M., & Macleod, C. (2012). Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings (PART 1 ed., pp. 280-287). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7663 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-34475-6_34

Gerrard, Claire E. ; McCall, John ; Coghill, George M. et al. / Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 1. ed. 2012. pp. 280-287 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks",

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.",

keywords = "Artificial Neural Networks, Artificial Reaction Networks, Biochemical Networks, Cellular Intelligence",

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Gerrard, CE, McCall, J, Coghill, GM & Macleod, C 2012, Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. in Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7663 LNCS, pp. 280-287, 19th International Conference on Neural Information Processing, ICONIP 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34475-6_34

Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. / Gerrard, Claire E.; McCall, John; Coghill, George M. et al.
Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 1. ed. 2012. p. 280-287 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7663 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks

AU - Gerrard, Claire E.

AU - McCall, John

AU - Coghill, George M.

AU - Macleod, Christopher

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

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KW - Artificial Reaction Networks

KW - Biochemical Networks

KW - Cellular Intelligence

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Gerrard CE, McCall J, Coghill GM, Macleod C. Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. PART 1 ed. 2012. p. 280-287. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-34475-6_34

Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks

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