Evolving Neural Controllers for Collective Robotic Inspection

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.

Published in:
Applied Soft Computing Technologies: The Challenge of Complexity, 721-733
Presented at:
9th Online World Conference on Soft Computing in Industrial Applications (WSC9)
Ajith Abraham, Bernard de Baets, Mario Köppen, Bertram Nickolay, Eds.

 Record created 2006-03-31, last modified 2018-03-17

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