Abstract
In an application where autonomous robots can amalgamate spontaneously into arbitrary organisms, the individual robots cannot know a priori at which location in an organism they will end up. If the organism is to be controlled autonomously by the constituent robots, an evolutionary algorithm that evolves the controllers can only develop a single genome that will have to suffice for every individual robot. However, the robots should show different behaviour depending on their position in an organism, meaning their phenotype should be different depending on their location. In this paper, we demonstrate a solution for this problem using the HyperNEAT generative encoding technique with differentiated genome expression. We develop controllers for organism locomotion with obstacle avoidance as a proof of concept. Finally, we identify promising directions for further research.
This is a preview of subscription content, log in via an institution to check access.
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
Proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, May 18-21. IEEE Press, Los Alamitos (2009)
Clune, J., Beckmann, B.E., Ofria, C., Pennock, R.T.: Evolving coordinated quadruped gaits with the hyperneat generative encoding. In: CEC-2009 [1]
D’Ambrosio, D.B., Stanley, K.O.: Generative encoding for multiagent learning. In: Ryan, C., Keijzer, M. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2008). ACM, New York (2008)
Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: A review. Neural Networks 21(4), 642–653 (2008) (Robotics and Neuroscience)
Kernbach, S., Meister, E., Scholz, O., Humza, R., Liedke, J., Ricotti, L., Jemai, J., Havlik, J., Liu, W.: Evolutionary robotics: The next-generation-platform for on-line and on-board artificial evolution. In: CEC-2009 [1], pp. 1079–1086
Marbach, D., Ijspeert, A.J.: Online optimization of modular robot locomotion. In: Proceedings of the IEEE Int. Conference on Mechatronics and Automation (ICMA 2005), pp. 248–253. IEEE Press, Los Alamitos (2005)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Stanley, K.O.: Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines 8(2), 131–162 (June 2007), Special issue on developmental systems
Stanley, K.O., D’Ambrosio, D.B., Gauci, J.: A hypercube-based indirect encoding for evolving large-scale neural networks. Artificial Life 15(2), 185–212 (2009)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2), 99–127 (2002)
Stanley, K.O., Miikkulainen, R.: A taxonomy for artificial embryogeny. Artificial Life 9(2), 93–130 (2003)
Waibel, M., Keller, L., Floreano, D.: Genetic Team Composition and Level of Selection in the Evolution of Cooperation. IEEE Transactions on Evolutionary Computation 13(3), 648–660 (2009)
Whitfield, P., Stoddard, D.M.: Hearing, Taste, and Smell; Pathways of Perception. Torstar Books, Inc., New York (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Haasdijk, E., Rusu, A.A., Eiben, A.E. (2010). HyperNEAT for Locomotion Control in Modular Robots. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds) Evolvable Systems: From Biology to Hardware. ICES 2010. Lecture Notes in Computer Science, vol 6274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15323-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-15323-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15322-8
Online ISBN: 978-3-642-15323-5
eBook Packages: Computer ScienceComputer Science (R0)