Abstract
Physics-based animal animations require data for realistic motion. This data is expensive to acquire through motion capture and inaccurate when estimated by an artist. Grammatical Evolution (GE) can be used to optimise pre-existing motion data or generate novel motions. Optimised motion data produces sustained locomotion in a physics-based model. To explore the use of GE for gait optimisation, the motion data of a walking horse, from a veterinary publication, is optimised for a physics-based horse model. The results of several grammars are presented and discussed. GE was found to be successful for optimising motion data using a grammar based on the concatenation of sinusoidal functions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Muybridge, E.: Horses and Other Animals in Motion. Dover Publications, New York (1985)
Back, W., Clayton, H.M.: Equine Locomotion. Harcourt Publishers (2001)
Alexander, R.M., Jayes, A.S.: A Dynamic Similarity Hypothesis for the Gaits of Quadrupedal Mammals. Journal of Zoology (London) 201, 135–152 (1983)
Parent, R.: Computer Animation: Algorithms and Techniques. The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling. Morgan Kaufmann, San Francisco (2002)
Kiguchi, K., Kusumoto, Y., Watanabe, K., Izumi, K., Fukuda, T.: Energy-Optimal Gait Analysis of Quadruped Robots. In: Artificial Life and Robotics, vol. 6, pp. 120–125. Springer, Japan (2002)
Garder, L.M., Høvin, M.E.: Robot Gaits Evolved by Combining Genetic Algorithms and Binary Hill Climbing. In: Genetic and Evolutionary Computation Conference 2006, pp. 1165–1170. ACM, New York (2006)
Xu, K., Chen, X., Liu, W., Williams, M.: Legged Robot Gait Locus Generation Based on Genetic Algorithms. In: Proc. International Symposium on Practical Cognitive Agents and Robots, vol. 213, pp. 51–62. ACM, New York (2006)
Smith, R.: Open Dynamics Engine v0.5 User Guide (2006)
Brabazon, A., O’Neill, M.: Biologically Inspired Algorithms for Financial Modelling. Springer, Heidelberg (2006)
O’Neill, M., Ryan, C.: Grammatical Evolution. Genetic Programming Series. Kluwer Academic Publishers, Dordrecht (2003)
GEVA: Grammatical Evolution in Java, http://ncra.ucd.ie/geva
Murphy, J.E., Carr, H., O’Neill, M.: Grammatical Evolution for Gait Retargeting. In: Proc. Theory and Practice of Computer Graphics 2008. Eurographics, pp. 159–162 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Murphy, J.E., O’Neill, M., Carr, H. (2009). Exploring Grammatical Evolution for Horse Gait Optimisation. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds) Genetic Programming. EuroGP 2009. Lecture Notes in Computer Science, vol 5481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01181-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-01181-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01180-1
Online ISBN: 978-3-642-01181-8
eBook Packages: Computer ScienceComputer Science (R0)