--- layout: default --- Publication details A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots Tim Taylor 2004 Abstract A decentralised real-time controller for a group of robots is presented, the design of which is inspired by biological genetic regulatory networks. A genetic algorithm (GA) is used to automatically evolve controllers for specific tasks. Results of initial experiments are presented and analysed, which demonstrate that it is possible to successfully evolve the controllers to achieve a simple clustering task. Performance is robust under a variety of parameter choices for the GA and controller. Full text Author preprint: pdf Reference Taylor, T. (2004). A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots. In F. Groen, N. Amato, A. Bonarini, E. Yoshida, & B. Kröse (Eds.), Intelligent Autonomous Systems 8 (Proceedings of IAS-8) (pp. 403–412). Amsterdam: IOS Press. BibTeX @inproceedings{taylor2004genetic, author = {Taylor, Tim}, title = {A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots}, booktitle = {Intelligent Autonomous Systems 8 (Proceedings of IAS-8)}, year = {2004}, editor = {Groen, Frans and Amato, Nancy and Bonarini, Andrea and Yoshida, Eiichi and Kr\"{o}se, Ben}, pages = {403-412}, address = {Amsterdam}, publisher = {{IOS} Press}, category = {conference}, keywords = {grn, hydra, robots} } Related publications
  1. Taylor, T., Ottery, P., & Hallam, J. (2007). An approach to time- and space-differentiated pattern formation in multi-robot systems. In M. S. Wilson, F. Labrosse, U. Nehmzow, C. Melhuish, & M. Witkowski (Eds.), TAROS 2007: Proceedings of Towards Autonomous Robotic Systems 2007 (pp. 160–167). Department of Computer Science, University of Wales, Aberystwyth.
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  2. Taylor, T., Ottery, P., & Hallam, J. (2007). Pattern formation for multi-robot applications: Robust, self-repairing systems inspired by genetic regulatory networks and cellular self-organisation (Informatics Research Report No. EDI-INF-RR-0971). University of Edinburgh.
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  3. Konidaris, G., Taylor, T., & Hallam, J. (2007). HydroGen: Automatically Generating Self-Assembly Code for Hydron Units. In R. Alami, H. Asama, & R. Chatila (Eds.), Distributed Autonomous Robotic Systems 6 (Proceedings of the Seventh International Symposium on Distributed Autonomous Robotic Systems, DARS04) (pp. 33–42). https://doi.org/10.1007/978-4-431-35873-2_4
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  4. Stewart, F., Taylor, T., & Konidaris, G. (2005). METAMorph: Experimenting with Genetic Regulatory Networks for Artificial Development. In M. S. Capcarrère, A. A. Freitas, P. J. Bentley, C. G. Johnson, & J. Timmis (Eds.), Advances in Artificial Life — 8th European Conference, ECAL 2005 (pp. 108–117). https://doi.org/10.1007/11553090_12
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  5. Østergaard, E. H., Christensen, D. J., Eggenberger, P., Taylor, T., Ottery, P., & Lund, H. H. (2005). HYDRA: From Cellular Biology to Shape-Changing Artefacts. In W. Duch, J. Kacprzyk, E. Oja, & S. Zadrożny (Eds.), Artificial Neural Networks: Biological Inspirations – ICANN 2005 (pp. 275–281). https://doi.org/10.1007/11550822_44
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  6. Taylor, T. (1993). Learning to Coordinate Behaviours on a Four-Legged Robot (Master's thesis). Department of Artificial Intelligence, University of Edinburgh.
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