Buchanan Berumen, Edgar orcid.org/0000-0001-6587-8808, Le Goff, Léni, Li, Wei orcid.org/0000-0001-9786-585X et al. (8 more authors) (2020) Bootstrapping artificial evolution to design robots for autonomous fabrication. MDPI Robotics. ISSN 2218-6581
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, The Author(s). |
Keywords: | evolutionary robotics,autonomous robot evolution,autonomous robot fabrication,robot manufacturability |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Funding Information: | Funder Grant number EPSRC EP/R03561X/1 |
Depositing User: | Pure (York) |
Date Deposited: | 02 Dec 2020 13:30 |
Last Modified: | 19 Nov 2024 00:39 |
Published Version: | https://doi.org/10.3390/robotics9040106 |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.3390/robotics9040106 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168646 |