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Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication

1
Department of Electronic Engineering, University of York, York YO10 5DD, UK
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School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
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Bristol Robotics Laboratory, University of the West of England, Bristol BS34 8QZ, UK
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School of Computer Science, University of Sunderland, Sunderland SR1 3SD, UK
*
Author to whom correspondence should be addressed.
Robotics 2020, 9(4), 106; https://doi.org/10.3390/robotics9040106
Received: 30 October 2020 / Revised: 26 November 2020 / Accepted: 2 December 2020 / Published: 7 December 2020
(This article belongs to the Special Issue Evolutionary Robotics)
A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability. View Full-Text
Keywords: evolutionary robotics; autonomous robot evolution; autonomous robot fabrication; robot manufacturability evolutionary robotics; autonomous robot evolution; autonomous robot fabrication; robot manufacturability
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MDPI and ACS Style

Buchanan, E.; Le Goff, L.K.; Li, W.; Hart, E.; Eiben, A.E.; De Carlo, M.; Winfield, A.F.; Hale, M.F.; Woolley, R.; Angus, M.; Timmis, J.; Tyrrell, A.M. Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication. Robotics 2020, 9, 106. https://doi.org/10.3390/robotics9040106

AMA Style

Buchanan E, Le Goff LK, Li W, Hart E, Eiben AE, De Carlo M, Winfield AF, Hale MF, Woolley R, Angus M, Timmis J, Tyrrell AM. Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication. Robotics. 2020; 9(4):106. https://doi.org/10.3390/robotics9040106

Chicago/Turabian Style

Buchanan, Edgar, Léni K. Le Goff, Wei Li, Emma Hart, Agoston E. Eiben, Matteo De Carlo, Alan F. Winfield, Matthew F. Hale, Robert Woolley, Mike Angus, Jon Timmis, and Andy M. Tyrrell. 2020. "Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication" Robotics 9, no. 4: 106. https://doi.org/10.3390/robotics9040106

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