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.
Image Caption: Artificially Evolved Robots that Efficiently Self-Organize Tasks
Image Credit: Ferrante et al.
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.
Press-only preview: https:/
Contact: Eliseo Ferrante
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.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact firstname.lastname@example.org.
PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.