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Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks

Soft robots can create complicated structures and functions for rehabilitation. The posture perception of soft actuators is critical for performing closed-loop control for a precise location. It is essential to have a sensor with both soft and flexible characteristics that does not affect the moveme...

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Autores principales: Shu, Jing, Wang, Junming, Lau, Sanders Cheuk Yin, Su, Yujie, Heung, Kelvin Ho Lam, Shi, Xiangqian, Li, Zheng, Tong, Raymond Kai-yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611759/
https://www.ncbi.nlm.nih.gov/pubmed/36298057
http://dx.doi.org/10.3390/s22207705
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author Shu, Jing
Wang, Junming
Lau, Sanders Cheuk Yin
Su, Yujie
Heung, Kelvin Ho Lam
Shi, Xiangqian
Li, Zheng
Tong, Raymond Kai-yu
author_facet Shu, Jing
Wang, Junming
Lau, Sanders Cheuk Yin
Su, Yujie
Heung, Kelvin Ho Lam
Shi, Xiangqian
Li, Zheng
Tong, Raymond Kai-yu
author_sort Shu, Jing
collection PubMed
description Soft robots can create complicated structures and functions for rehabilitation. The posture perception of soft actuators is critical for performing closed-loop control for a precise location. It is essential to have a sensor with both soft and flexible characteristics that does not affect the movement of a soft actuator. This paper presents a novel end-to-end posture perception method that employs flexible sensors with kirigami-inspired structures and long short-term memory (LSTM) neural networks. The sensors were developed with conductive sponge materials. With one-step calibration from the sensor output, the posture of the soft actuator could be calculated by the LSTM network. The method was validated by attaching the developed sensors to a soft fiber-reinforced bending actuator. The results showed the accuracy of posture prediction of sponge sensors with three kirigami-inspired structures ranged from 0.91 to 0.97 in terms of [Formula: see text]. The sponge sensors only generated a resistive torque value of 0.96 mNm at the maximum bending position when attached to a soft actuator, which would minimize the effect on actuator movement. The kirigami-inspired flexible sponge sensor could in future enhance soft robotic development.
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spelling pubmed-96117592022-10-28 Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks Shu, Jing Wang, Junming Lau, Sanders Cheuk Yin Su, Yujie Heung, Kelvin Ho Lam Shi, Xiangqian Li, Zheng Tong, Raymond Kai-yu Sensors (Basel) Article Soft robots can create complicated structures and functions for rehabilitation. The posture perception of soft actuators is critical for performing closed-loop control for a precise location. It is essential to have a sensor with both soft and flexible characteristics that does not affect the movement of a soft actuator. This paper presents a novel end-to-end posture perception method that employs flexible sensors with kirigami-inspired structures and long short-term memory (LSTM) neural networks. The sensors were developed with conductive sponge materials. With one-step calibration from the sensor output, the posture of the soft actuator could be calculated by the LSTM network. The method was validated by attaching the developed sensors to a soft fiber-reinforced bending actuator. The results showed the accuracy of posture prediction of sponge sensors with three kirigami-inspired structures ranged from 0.91 to 0.97 in terms of [Formula: see text]. The sponge sensors only generated a resistive torque value of 0.96 mNm at the maximum bending position when attached to a soft actuator, which would minimize the effect on actuator movement. The kirigami-inspired flexible sponge sensor could in future enhance soft robotic development. MDPI 2022-10-11 /pmc/articles/PMC9611759/ /pubmed/36298057 http://dx.doi.org/10.3390/s22207705 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shu, Jing
Wang, Junming
Lau, Sanders Cheuk Yin
Su, Yujie
Heung, Kelvin Ho Lam
Shi, Xiangqian
Li, Zheng
Tong, Raymond Kai-yu
Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks
title Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks
title_full Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks
title_fullStr Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks
title_full_unstemmed Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks
title_short Soft Robots’ Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks
title_sort soft robots’ dynamic posture perception using kirigami-inspired flexible sensors with porous structures and long short-term memory (lstm) neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9611759/
https://www.ncbi.nlm.nih.gov/pubmed/36298057
http://dx.doi.org/10.3390/s22207705
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