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  4. Evolving Neural Controllers for Collective Robotic Inspection
 
conference paper

Evolving Neural Controllers for Collective Robotic Inspection

Zhang, Yizhen
•
Antonsson, E. K.
•
Martinoli, Alcherio  
2006
Applied Soft Computing Technologies: The Challenge of Complexity
9th Online World Conference on Soft Computing in Industrial Applications (WSC9)

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.

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