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Model Analysis of Robotic Soft Arms including External Force Effects

Because robotic soft arms have a high power-to-weight ratio, low cost, and ease of manufacturability, increasing numbers of researchers have begun to focus on their characteristics in recent years. However, many urgent problems remain to be resolved. For example, soft arms are made of hyperelastic m...

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Detalles Bibliográficos
Autores principales: Chen, Zhi, Liu, Zhong, Han, Xingguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950771/
https://www.ncbi.nlm.nih.gov/pubmed/35334642
http://dx.doi.org/10.3390/mi13030350
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author Chen, Zhi
Liu, Zhong
Han, Xingguo
author_facet Chen, Zhi
Liu, Zhong
Han, Xingguo
author_sort Chen, Zhi
collection PubMed
description Because robotic soft arms have a high power-to-weight ratio, low cost, and ease of manufacturability, increasing numbers of researchers have begun to focus on their characteristics in recent years. However, many urgent problems remain to be resolved. For example, soft arms are made of hyperelastic material, making it difficult to obtain accurate model predictions of the soft arm shape. This paper proposes a new modeling method for soft arms, combining the constant curvature model with Euler–Bernoulli beam theory. By combining these two modeling methods, we can quickly solve for the soft arm deformation under the action of an external force. This paper also presents an experimental platform based on a cable-driven soft arm to verify the validity of the proposed model. We carried out model verification experiments to test for different external effects. Experimental results show that the maximum error of our proposed soft arm deformation model is between 2.86% and 8.75%, demonstrating its effectiveness.
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spelling pubmed-89507712022-03-26 Model Analysis of Robotic Soft Arms including External Force Effects Chen, Zhi Liu, Zhong Han, Xingguo Micromachines (Basel) Article Because robotic soft arms have a high power-to-weight ratio, low cost, and ease of manufacturability, increasing numbers of researchers have begun to focus on their characteristics in recent years. However, many urgent problems remain to be resolved. For example, soft arms are made of hyperelastic material, making it difficult to obtain accurate model predictions of the soft arm shape. This paper proposes a new modeling method for soft arms, combining the constant curvature model with Euler–Bernoulli beam theory. By combining these two modeling methods, we can quickly solve for the soft arm deformation under the action of an external force. This paper also presents an experimental platform based on a cable-driven soft arm to verify the validity of the proposed model. We carried out model verification experiments to test for different external effects. Experimental results show that the maximum error of our proposed soft arm deformation model is between 2.86% and 8.75%, demonstrating its effectiveness. MDPI 2022-02-23 /pmc/articles/PMC8950771/ /pubmed/35334642 http://dx.doi.org/10.3390/mi13030350 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
Chen, Zhi
Liu, Zhong
Han, Xingguo
Model Analysis of Robotic Soft Arms including External Force Effects
title Model Analysis of Robotic Soft Arms including External Force Effects
title_full Model Analysis of Robotic Soft Arms including External Force Effects
title_fullStr Model Analysis of Robotic Soft Arms including External Force Effects
title_full_unstemmed Model Analysis of Robotic Soft Arms including External Force Effects
title_short Model Analysis of Robotic Soft Arms including External Force Effects
title_sort model analysis of robotic soft arms including external force effects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950771/
https://www.ncbi.nlm.nih.gov/pubmed/35334642
http://dx.doi.org/10.3390/mi13030350
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