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Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination

Biological microswimmers can coordinate their motions to exploit their fluid environment—and each other—to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers....

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Autores principales: Liu, Yangzhe, Zou, Zonghao, Pak, On Shun, Tsang, Alan C. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256736/
https://www.ncbi.nlm.nih.gov/pubmed/37296306
http://dx.doi.org/10.1038/s41598-023-36305-y
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author Liu, Yangzhe
Zou, Zonghao
Pak, On Shun
Tsang, Alan C. H.
author_facet Liu, Yangzhe
Zou, Zonghao
Pak, On Shun
Tsang, Alan C. H.
author_sort Liu, Yangzhe
collection PubMed
description Biological microswimmers can coordinate their motions to exploit their fluid environment—and each other—to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers. Here we probe the emergence of such cooperative behaviors among artificial microswimmers endowed with artificial intelligence. We present the first use of a deep reinforcement learning approach to empower the cooperative locomotion of a pair of reconfigurable microswimmers. The AI-advised cooperative policy comprises two stages: an approach stage where the swimmers get in close proximity to fully exploit hydrodynamic interactions, followed a synchronization stage where the swimmers synchronize their locomotory gaits to maximize their overall net propulsion. The synchronized motions allow the swimmer pair to move together coherently with an enhanced locomotion performance unattainable by a single swimmer alone. Our work constitutes a first step toward uncovering intriguing cooperative behaviors of smart artificial microswimmers, demonstrating the vast potential of reinforcement learning towards intelligent autonomous manipulations of multiple microswimmers for their future biomedical and environmental applications.
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spelling pubmed-102567362023-06-11 Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination Liu, Yangzhe Zou, Zonghao Pak, On Shun Tsang, Alan C. H. Sci Rep Article Biological microswimmers can coordinate their motions to exploit their fluid environment—and each other—to achieve global advantages in their locomotory performance. These cooperative locomotion require delicate adjustments of both individual swimming gaits and spatial arrangements of the swimmers. Here we probe the emergence of such cooperative behaviors among artificial microswimmers endowed with artificial intelligence. We present the first use of a deep reinforcement learning approach to empower the cooperative locomotion of a pair of reconfigurable microswimmers. The AI-advised cooperative policy comprises two stages: an approach stage where the swimmers get in close proximity to fully exploit hydrodynamic interactions, followed a synchronization stage where the swimmers synchronize their locomotory gaits to maximize their overall net propulsion. The synchronized motions allow the swimmer pair to move together coherently with an enhanced locomotion performance unattainable by a single swimmer alone. Our work constitutes a first step toward uncovering intriguing cooperative behaviors of smart artificial microswimmers, demonstrating the vast potential of reinforcement learning towards intelligent autonomous manipulations of multiple microswimmers for their future biomedical and environmental applications. Nature Publishing Group UK 2023-06-09 /pmc/articles/PMC10256736/ /pubmed/37296306 http://dx.doi.org/10.1038/s41598-023-36305-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Yangzhe
Zou, Zonghao
Pak, On Shun
Tsang, Alan C. H.
Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination
title Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination
title_full Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination
title_fullStr Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination
title_full_unstemmed Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination
title_short Learning to cooperate for low-Reynolds-number swimming: a model problem for gait coordination
title_sort learning to cooperate for low-reynolds-number swimming: a model problem for gait coordination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256736/
https://www.ncbi.nlm.nih.gov/pubmed/37296306
http://dx.doi.org/10.1038/s41598-023-36305-y
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