Soft Pneumatic Muscles: Revolutionizing Human Assistive Devices with Geometric Design and Intelligent Control
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Published:2023-07-16
Issue:7
Volume:14
Page:1431
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ISSN:2072-666X
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Container-title:Micromachines
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language:en
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Short-container-title:Micromachines
Author:
Elsamanty Mahmoud12ORCID, Hassaan Mohamed A.1ORCID, Orban Mostafa134ORCID, Guo Kai34ORCID, Yang Hongbo34, Abdrabbo Saber1, Selmy Mohamed5ORCID
Affiliation:
1. Mechanical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt 2. Mechatronics and Robotics Department, School of Innovative Design Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt 3. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China 4. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China 5. Electrical Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt
Abstract
Soft robotics, a recent advancement in robotics systems, distinguishes itself by utilizing soft and flexible materials like silicon rubber, prioritizing safety during human interaction, and excelling in handling complex or delicate objects. Soft pneumatic actuators, a prevalent type of soft robot, are the focus of this paper. A new geometrical parameter for soft artificial pneumatic muscles is introduced, enabling the prediction of actuation behavior using analytical models based on specific design parameters. The study investigated the impact of the chamber pitch parameter and actuation conditions on the deformation direction and internal stress of three tested soft pneumatic muscle (SPM) models. Simulation involved the modeling of hyperelastic materials using finite element analysis. Additionally, an artificial neural network (ANN) was employed to predict pressure values in three chambers at desired Cartesian positions. The trained ANN model demonstrated exceptional performance. It achieved high accuracy with training, validation, and testing residuals of 99.58%, 99.89%, and 99.79%, respectively. During the validation simulations and neural network results, the maximum errors in the x, y, and z coordinates were found to be 9.3%, 7.83%, and 8.8%, respectively. These results highlight the successful performance and efficacy of the trained ANN model in accurately predicting pressure values for the desired positions in the soft pneumatic muscles.
Funder
Natural Fund of Shandong Province Chinese Academy of Sciences (CAS) President’s International Fellowship Initiative
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
Reference48 articles.
1. Soft Robotics;Whitesides;Angew. Chem. Int. Ed.,2018 2. Soft Pneumatic Actuators for Rehabilitation;Belforte;Actuators,2014 3. Joe, S., Totaro, M., Wang, H., and Beccai, L. (2021). Development of the Ultralight Hybrid Pneumatic Artificial Muscle: Modelling and optimization. PLoS ONE, 16. 4. Su, H., Hou, X., Zhang, X., Qi, W., Cai, S., Xiong, X., and Guo, J. (2022). Pneumatic Soft Robots: Challenges and Benefits. Actuators, 11. 5. Soft robotics;Eiberger;IEEE Robot. Autom. Mag.,2008
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