Publication details

Pattern formation for multi-robot applications: Robust, self-repairing systems inspired by genetic regulatory networks and cellular self-organisation

Tim Taylor, Peter Ottery, John Hallam
2007
Abstract

This work concerns a biologically-inspired approach to self-assembly and pattern formation in multi-robot systems. In previous work the authors have recently studied two different approaches to multi-robot control, one based upon the evolution of controllers modelled as genetic regulatory networks (GRNs), and the other based upon a model of self-organisation in aggregates of biological cells mediated by cellular adhesion molecules (CAMs). In the current work, a hybrid GRN-CAM controller is introduced, which captures the advantages, and overcomes the disadvantages, or both of the original controllers; it combines the adaptability of the evolutionary process with the robustness of an underlying self-organising dynamics. The performance of the new controller is investigated and compared with the previous ones. For example, one experiment involves the evolution of a robot cluster that can stably maintain two different spatial patterns, switching between the two upon sensing an external signal. Another experiment involves the evolution of a cluster in which individual robots develop differentiated states despite having indentical controllers (which could be used as a starting point for functional specialisation of robots within the cluster). The results show that the combined GRN-CAM controller is more flexible and robust than either the GRN controller or the CAM controller by itself, and can produce more complex spatiotemporal behaviours. The GRN-CAM controllers are also potentially portable to robotic systems other than those for which they were evolved, as long as the new system implements the underlying CAM model of self-organisation. Some technical issues regarding the implementation of the GRN and joint GRN-CAM systems are also discussed, including the use of "smart mutation" operators to improve the speed of evolution of GRNs, and evolving the rate of dynamics of the GRN controller to suit the particular task in hand.

Full text
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Reference

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.

BibTeX

@techreport{taylor2007pattern,
  author = {Taylor, Tim and Ottery, Peter and Hallam, John},
  title = {Pattern formation for multi-robot applications: Robust, self-repairing systems inspired by genetic regulatory networks and cellular self-organisation},
  institution = {University of Edinburgh},
  year = {2007},
  type = {Informatics Research Report},
  number = {EDI-INF-RR-0971},
  category = {techreport},
  keywords = {grn, hydra, robots}
}
Note

Record on departmental database: EDI-INF-RR-0971

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. 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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  3. 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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  4. 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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  5. 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.
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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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