Literature DB >> 33141694

Morphogenesis in robot swarms.

I Slavkov1,2, D Carrillo-Zapata3,4,5, N Carranza1,2, X Diego1,2,6, F Jansson7,8, J Kaandorp8, S Hauert3,5, J Sharpe9,2,6,10.   

Abstract

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.
Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Entities:  

Year:  2018        PMID: 33141694     DOI: 10.1126/scirobotics.aau9178

Source DB:  PubMed          Journal:  Sci Robot        ISSN: 2470-9476


  8 in total

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2.  Evolution of Brains and Computers: The Roads Not Taken.

Authors:  Ricard Solé; Luís F Seoane
Journal:  Entropy (Basel)       Date:  2022-05-09       Impact factor: 2.738

3.  Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics.

Authors:  Edmund R Hunt
Journal:  Front Robot AI       Date:  2020-03-16

4.  Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms.

Authors:  Andrea Roli; Antoine Ligot; Mauro Birattari
Journal:  Front Robot AI       Date:  2019-11-26

5.  Automatic Off-Line Design of Robot Swarms: A Manifesto.

Authors:  Mauro Birattari; Antoine Ligot; Darko Bozhinoski; Manuele Brambilla; Gianpiero Francesca; Lorenzo Garattoni; David Garzón Ramos; Ken Hasselmann; Miquel Kegeleirs; Jonas Kuckling; Federico Pagnozzi; Andrea Roli; Muhammad Salman; Thomas Stützle
Journal:  Front Robot AI       Date:  2019-07-19

6.  Reincarnations of the phase separation problem.

Authors:  Ruo-Yu Dong; Steve Granick
Journal:  Nat Commun       Date:  2021-02-10       Impact factor: 14.919

Review 7.  Synthetic living machines: A new window on life.

Authors:  Mo R Ebrahimkhani; Michael Levin
Journal:  iScience       Date:  2021-05-04

8.  Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms.

Authors:  Ken Hasselmann; Antoine Ligot; Julian Ruddick; Mauro Birattari
Journal:  Nat Commun       Date:  2021-07-16       Impact factor: 14.919

  8 in total

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