基于潜意识表征的学习控制器,用于压力驱动软执行器的气动和液压双重驱动

IF 6.4 2区 计算机科学 Q1 ROBOTICS
Soft Robotics Pub Date : 2024-02-01 Epub Date: 2023-08-17 DOI:10.1089/soro.2022.0224
Taku Sugiyama, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe
{"title":"基于潜意识表征的学习控制器,用于压力驱动软执行器的气动和液压双重驱动","authors":"Taku Sugiyama, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe","doi":"10.1089/soro.2022.0224","DOIUrl":null,"url":null,"abstract":"<p><p>The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.</p>","PeriodicalId":48685,"journal":{"name":"Soft Robotics","volume":" ","pages":"105-117"},"PeriodicalIF":6.4000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880272/pdf/","citationCount":"0","resultStr":"{\"title\":\"Latent Representation-Based Learning Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-Driven Soft Actuators.\",\"authors\":\"Taku Sugiyama, Kyo Kutsuzawa, Dai Owaki, Mitsuhiro Hayashibe\",\"doi\":\"10.1089/soro.2022.0224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.</p>\",\"PeriodicalId\":48685,\"journal\":{\"name\":\"Soft Robotics\",\"volume\":\" \",\"pages\":\"105-117\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10880272/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soft Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1089/soro.2022.0224\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soft Robotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1089/soro.2022.0224","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/17 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

摘要

压力驱动软执行器(PSA)的气动和液压双驱动技术具有开发新型实用软机器人和扩大软机器人应用范围的潜力,因此前景广阔。然而,空气和水的物理特性大相径庭,这使得快速适应所选执行方法并实现与方法无关的精确控制性能具有挑战性。在此,我们提出了一种用于双重致动的新颖的、基于 "LAtent 表征 "的前馈神经网络(LAR-FNN)。LAR-FNN 由自动编码器 (AE) 和前馈神经网络 (FNN) 组成。自动编码器从 30 秒的步进响应中生成 PSA 的潜在表示。随后,前馈神经网络提供目标 PSA 的单独逆模型,并利用潜表征计算前馈控制输入。PSA 的实验结果表明,LAR-FNN 可以通过单个神经网络满足双驱动控制的要求(即无论采用何种驱动方法,都能在较短的适应时间内实现精确的控制性能)。结果表明,LAR-FNN 可以为软双作用机器人的开发和软机器人领域做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latent Representation-Based Learning Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-Driven Soft Actuators.

The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Soft Robotics
Soft Robotics ROBOTICS-
CiteScore
15.50
自引率
5.10%
发文量
128
期刊介绍: Soft Robotics (SoRo) stands as a premier robotics journal, showcasing top-tier, peer-reviewed research on the forefront of soft and deformable robotics. Encompassing flexible electronics, materials science, computer science, and biomechanics, it pioneers breakthroughs in robotic technology capable of safe interaction with living systems and navigating complex environments, natural or human-made. With a multidisciplinary approach, SoRo integrates advancements in biomedical engineering, biomechanics, mathematical modeling, biopolymer chemistry, computer science, and tissue engineering, offering comprehensive insights into constructing adaptable devices that can undergo significant changes in shape and size. This transformative technology finds critical applications in surgery, assistive healthcare devices, emergency search and rescue, space instrument repair, mine detection, and beyond.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信