Artificially evolved robots that efficiently self-organize tasks

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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. Credit: Ferrante et al.

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

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

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.

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