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Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning
Underwater snake robots have received attention because of their unique mechanics and locomotion patterns. Given their highly redundant degrees of freedom, designing an energy-efficient gait has been a main challenge for the long-term autonomy of underwater snake robots. We propose a gait design met...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493006/ https://www.ncbi.nlm.nih.gov/pubmed/36158602 http://dx.doi.org/10.3389/frobt.2022.957931 |
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author | Zheng, Chu Li , Guanda Hayashibe, Mitsuhiro |
author_facet | Zheng, Chu Li , Guanda Hayashibe, Mitsuhiro |
author_sort | Zheng, Chu |
collection | PubMed |
description | Underwater snake robots have received attention because of their unique mechanics and locomotion patterns. Given their highly redundant degrees of freedom, designing an energy-efficient gait has been a main challenge for the long-term autonomy of underwater snake robots. We propose a gait design method for an underwater snake robot based on deep reinforcement learning and curriculum learning. For comparison, we consider the gait generated by a conventional parametric gait equation controller as the baseline. Furthermore, inspired by the joints of living organisms, we consider elasticity (stiffness) in the joints of the snake robot to verify whether it contributes to the generation of energy efficiency in the underwater gait. We first demonstrate that the deep reinforcement learning controller can produce a more energy-efficient gait than the gait equation controller in underwater locomotion, by finding the control patterns which maximize the effect of energy efficiency through the exploitation of joint elasticity. In addition, appropriate joint elasticity can increase the maximum velocity achievable by a snake robot. Finally, simulation results in different liquid environments confirm that the deep reinforcement learning controller is superior to the gait equation controller, and it can find adaptive energy-efficient motion even when the liquid environment is changed. The video can be viewed at https://youtu.be/wpwQihhntEY. |
format | Online Article Text |
id | pubmed-9493006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94930062022-09-23 Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning Zheng, Chu Li , Guanda Hayashibe, Mitsuhiro Front Robot AI Robotics and AI Underwater snake robots have received attention because of their unique mechanics and locomotion patterns. Given their highly redundant degrees of freedom, designing an energy-efficient gait has been a main challenge for the long-term autonomy of underwater snake robots. We propose a gait design method for an underwater snake robot based on deep reinforcement learning and curriculum learning. For comparison, we consider the gait generated by a conventional parametric gait equation controller as the baseline. Furthermore, inspired by the joints of living organisms, we consider elasticity (stiffness) in the joints of the snake robot to verify whether it contributes to the generation of energy efficiency in the underwater gait. We first demonstrate that the deep reinforcement learning controller can produce a more energy-efficient gait than the gait equation controller in underwater locomotion, by finding the control patterns which maximize the effect of energy efficiency through the exploitation of joint elasticity. In addition, appropriate joint elasticity can increase the maximum velocity achievable by a snake robot. Finally, simulation results in different liquid environments confirm that the deep reinforcement learning controller is superior to the gait equation controller, and it can find adaptive energy-efficient motion even when the liquid environment is changed. The video can be viewed at https://youtu.be/wpwQihhntEY. Frontiers Media S.A. 2022-09-08 /pmc/articles/PMC9493006/ /pubmed/36158602 http://dx.doi.org/10.3389/frobt.2022.957931 Text en Copyright © 2022 Zheng, Li and Hayashibe. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Robotics and AI Zheng, Chu Li , Guanda Hayashibe, Mitsuhiro Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning |
title | Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning |
title_full | Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning |
title_fullStr | Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning |
title_full_unstemmed | Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning |
title_short | Joint elasticity produces energy efficiency in underwater locomotion: Verification with deep reinforcement learning |
title_sort | joint elasticity produces energy efficiency in underwater locomotion: verification with deep reinforcement learning |
topic | Robotics and AI |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9493006/ https://www.ncbi.nlm.nih.gov/pubmed/36158602 http://dx.doi.org/10.3389/frobt.2022.957931 |
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