Evolving Neural Controllers for Collective Robotic Inspection

• Presented at: 9th Online World Conference on Soft Computing in Industrial Applications (WSC9)
• Published in: Applied Soft Computing Technologies: The Challenge of Complexity, p. 721-733
• Series: Series Advances in Soft Computing
• Publication date: 2006

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.

Keywords: swarm robotics ; collective inspection ; genetic algorithms ; evolutionary computation

Note:

Ajith Abraham, Bernard de Baets, Mario Köppen, Bertram Nickolay, Eds.

Reference

• SWIS-CONF-2006-006
• doi:10.1007/3-540-31662-0_55
• URL: http://www.springer.com/sgw/cda/frontpage/0,11855,1-40109-22-122343893-0,00.html

Record created on 2006-03-31, modified on 2016-08-08