Buchanan Berumen, Edgar orcid.org/0000-0001-6587-8808, Le Goff, Léni, Hart, Emma et al. (8 more authors) (Accepted: 2020) Evolution of Diverse, Manufacturable Robot Body Plans. In: 2020 IEEE Symposium Series on Computational Intelligence. (In Press)
Abstract
Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new challenges, in that robot models that can be instantiated from an encoding in simulation might not be manufacturable in practice (due to constraints associated with the 3D printing and/or automated assembly processes). We introduce a representation for evolving (wheeled) robots with a printed plastic skeleton, and evaluate three variants of a novelty-search algorithm in terms of their ability to produce populations of manufacturable but diverse robots. While the set of manufacturable robots discovered represent only a small fraction of the overall search space of all robots, all methods are shown to be capable of generating a diverse population of manufacturable robots that we conjecture is large enough to seed an evolving robotic ecosystem.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Keywords: | evolutionary robotics,autonomous robot fabrication,autonomous robot evolution,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: | 30 Sep 2020 10:20 |
Last Modified: | 08 Feb 2025 00:12 |
Status: | In Press |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166130 |
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