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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.

 
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Reference
<span id="taylor2007pattern">Taylor, T., Ottery, P., &amp; Hallam, J. (2007). <i>Pattern formation for multi-robot applications: Robust, self-repairing systems inspired by genetic regulatory networks and cellular self-organisation</i> (Informatics Research Report No. EDI-INF-RR-0971). University of Edinburgh.</span>

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}
}

   
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<div class="overflow-scroll"><span id="taylor1993learning">Taylor, T. (1993). <i><b>Learning to Coordinate Behaviours on a Four-Legged Robot</b></i> (Master's thesis). Department of Artificial Intelligence, University of Edinburgh.</span></div>

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