Practical hardware for evolvable robots.

autonomous robot fabrication evolutionary robotics hardware constraints hardware design modular robots robot manufacturability

Journal

Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350

Informations de publication

Date de publication:
2023
Historique:
received: 14 04 2023
accepted: 07 08 2023
medline: 6 9 2023
pubmed: 6 9 2023
entrez: 6 9 2023
Statut: epublish

Résumé

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.

Identifiants

pubmed: 37670906
doi: 10.3389/frobt.2023.1206055
pii: 1206055
pmc: PMC10475714
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1206055

Informations de copyright

Copyright © 2023 Angus, Buchanan, Le Goff, Hart, Eiben, De Carlo, Winfield, Hale, Woolley, Timmis and Tyrrell.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Références

Patterns (N Y). 2022 Oct 14;3(10):100588
pubmed: 36277819
Artif Life. 2017 Spring;23(2):206-235
pubmed: 28513201
PLoS One. 2020 May 29;15(5):e0233848
pubmed: 32470076
Proc Natl Acad Sci U S A. 2020 Jan 28;117(4):1853-1859
pubmed: 31932426
Front Robot AI. 2021 Jul 05;8:699814
pubmed: 34291092
PLoS One. 2015 Jun 19;10(6):e0128444
pubmed: 26091255
Front Robot AI. 2021 Jul 27;8:696452
pubmed: 34386525

Auteurs

Mike Angus (M)

School of Physics, Engineering and Technology, University of York, York, United Kingdom.

Edgar Buchanan (E)

School of Physics, Engineering and Technology, University of York, York, United Kingdom.

Léni K Le Goff (LK)

School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom.

Emma Hart (E)

School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom.

Agoston E Eiben (AE)

Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

Matteo De Carlo (M)

Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

Alan F Winfield (AF)

Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom.

Matthew F Hale (MF)

Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom.

Robert Woolley (R)

School of Physics, Engineering and Technology, University of York, York, United Kingdom.

Jon Timmis (J)

School of Computer Science, University of Sunderland, Sunderland, United Kingdom.

Andy M Tyrrell (AM)

School of Physics, Engineering and Technology, University of York, York, United Kingdom.

Classifications MeSH