Adaptive Dynamic Control of Quadrupedal Robotic Gaits with Artificial Reaction Networks

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.

[1]  Aude Billard,et al.  Biologically inspired neural controllers for motor control in a quadruped robot , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  DAVID TOTH.  Evolving Gaits for the Lynxmotion Hexapod II Robot , 2003 .

[3]  B. Kholodenko.  Cell-signalling dynamics in time and space , 2006, Nature Reviews Molecular Cell Biology.

[4]  A S Madhukumar,et al.  Significance of knowledge sources for a text-to-speech system for Indian languages , 1994 .

[5]  Toshiyuki Nakagaki,et al.  Amoebae anticipate periodic events. , 2008, Physical review letters.

[6]  Baojun Wang,et al.  Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology , 2011, Nature communications.

[7]  J. J. Collins,et al.  Hard-wired central pattern generators for quadrupedal locomotion , 1994, Biological Cybernetics.

[8]  M. Hildebrand.  Analysis of Asymmetrical Gaits , 1977 .

[9]  Upinder S Bhalla,et al.  Understanding complex signaling networks through models and metaphors. , 2003, Progress in biophysics and molecular biology.

[10]  D. Bray.  Protein molecules as computational elements in living cells , 1995, Nature.

[11]  J. Ross,et al.  Computational functions in biochemical reaction networks. , 1994, Biophysical journal.

[12]  B. Yegnanarayana,et al.  Artificial neural networks for pattern recognition , 1994 .

[13]  T. Nakagaki,et al.  Intelligence: Maze-solving by an amoeboid organism , 2000, Nature.

[14]  George M. Coghill,et al.  Artificial reaction networks , 2011 .

[15]  A. Dagg.  Gaits in mammals , 1973 .

[16]  Klaas J Hellingwerf,et al.  Bacterial observations: a rudimentary form of intelligence? , 2005, Trends in microbiology.

[17]  John G. L. Morris.  A biologist's physical chemistry , 1968 .

[18]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.