Public Release: 

Artificially evolved robots that efficiently self-organize tasks

Eliseo Ferrante and colleagues evolved complex robot behaviors using artificial evolution and detailed robotics simulations.

PLOS

Darwinian selection can be used to evolve robot controllers able to efficiently self-organize their tasks. Taking inspiration from the way in which ants organise their work and divide up tasks, Eliseo Ferrante and colleagues evolved complex robot behaviors using artificial evolution and detailed robotics simulations.

Just like social insects such as ants, bees or termites teams of robots display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group, says new research publishing in PLOS Computational Biology.

The field of 'swarm robotics' aims to use teams of small robots to explore complex environments, such as the moon or foreign planets. However, designing controllers that allow the robots to effectively organize themselves is no easy task.

The novel method developed by the team of scientists from the University of Leuven, the Free University of Brussels and the Middle East Technical University is based on grammatical evolution and Allows the evolution of behaviours that go beyond the complexity achieved before this study.

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Image Caption: Artificially Evolved Robots that Efficiently Self-Organize Tasks

Image Credit: Ferrante et al.

Image Link: https://www.plos.org/wp-content/uploads/2015/08/Okada-14-May.png

All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004273

Press-only preview: https://www.plos.org/wp-content/uploads/2015/08/pcbi.1004273.pdf

Contact: Eliseo Ferrante
Address: KULeuven
Biology
Naamsestraat 59
Leuven, B-3000
BELGIUM
Phone: +32488276842
Email: eliseo.ferrante@bio.kuleuven.be

Citation: Ferrante E, Turgut AE, Duéñez-Guzmán E, Dorigo M, Wenseleers T (2015) Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comput Biol 11(8): e1004273.doi:10.1371/journal.pcbi.1004273

Funding: EF, AETand TW acknowledge the European Science Foundation "H2Swarm" program and the KU Leuven for the IDO-BioCo3 project and KULeuven Excellence Center project PF/2010/007. EDG acknowledges the KU Leuven for the Grant F+/11/033. MD acknowledges the Fonds de la Recherche Scientifique--FNRS (F.R.S.-FNRS--http://www.fnrs.be) and the European Research Council (ERC--erc.europa.eu) ERC Advanced Grant "E-SWARM: Engineering Swarm Intelligence Systems" (grant 246939). AETacknowledges the Scientific and Technological Research Council of Turkey (Tubitak--http://www.tubitak.gov.tr/en) grant TUBITAK-2219. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

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