Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors

Quinn, Matt, Smith, Lincoln, Mayley, Giles and Husbands, Phil (2003) Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors. Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, 361 (1811). pp. 2321-2343. ISSN 1471-2962

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We report on recent work in which we employed artificial evolution to design neural network controllers for small, homogeneous teams of mobile autonomous robots. The robots were evolved to perform a formation-movement task from random starting positions, equipped only with infrared sensors. The dual constraints of homogeneity and minimal sensors make this a non-trivial task. We describe the behaviour of a successful system in which robots adopt and maintain functionally distinct roles in order to achieve the task. We believe this to be the first example of the use of artificial evolution to design coordinated, cooperative behaviour for real robots.

Item Type: Article
Additional Information: Originality: First successful use of evolutionary robotics methodology to develop coordinated cooperative behaviour in a team of real robots. Rigour: Methodology was extended to allow development of neural controllers for homogeneous teams of autonomous robots equipped with minimal local sensing. Despite severe constraints, the evolved system proved capable of organizing itself as a team, involving functionally distinct roles. Sufficient runs were undertaken to produce statistically highly significant results and prove the efficacy of the methodology. Significance: Demonstrates how to develop non-trivial behaviour with highly constrained minimal systems, with potentially important applications in space science and nano-robotics. Impact: Widely recognised as an important advance in the growing field of evolutionary robotics as evidenced by its inclusion in a number of significant recent international reviews of the field. 29 Google scholar citations, 15 Web of Science. Total Google scholar citations for this journal paper and its preceding conference paper and technical report are 64.
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Douglas Lincoln Smith
Date Deposited: 06 Feb 2012 20:38
Last Modified: 17 May 2012 14:49
URI: http://sro.sussex.ac.uk/id/eprint/27142
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