Zhang, Yizhen and Antonsson, Erik K. and Martinoli, Alcherio (2006) Evolving neural controllers for collective robotic inspection. In: Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing . Springer , Berlin , pp. 717-729. ISBN 3-540-31649-3 http://resolver.caltech.edu/CaltechAUTHORS:20110425-074717159
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In this paper, an automatic synthesis methodology based on evolutionary computation is applied to evolve neural controllers for a homogeneous team of miniature autonomous mobile robots. Both feed-forward and recurrent neural networks can be evolved with fixed or variable network topologies. The efficacy of the evolutionary methodology is demonstrated in the framework of a realistic case study on collective robotic inspection of regular structures, where the robots are only equipped with limited local on-board sensing and actuating capabilities. The neural controller solutions generated during evolutions are evaluated in a sensorbased embodied simulation environment with realistic noise. It is shown that the evolutionary algorithms are able to successfully synthesize a variety of novel neural controllers that could achieve performances comparable to a carefully hand-tuned, rule-based controller in terms of both average performance and robustness to noise.
|Item Type:||Book Section|
|Additional Information:||© 2006 Springer. This material is based upon work supported, in part, by NASA Glenn Research Center and by the Engineering Research Centers Program of the National Science Foundation under Award Number EEC-9402726. Martinoli A is currently sponsored by a Swiss NSF professorship.|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Tony Diaz|
|Deposited On:||03 Jun 2011 21:59|
|Last Modified:||03 Jun 2011 21:59|
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