<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>8</volume><submitter>Gollob SD</submitter><pubmed_abstract>In this paper, we present a generalized modeling tool for predicting the output force profile of vacuum-powered soft actuators using a simplified geometrical approach and the principle of virtual work. Previous work has derived analytical formulas to model the force-contraction profile of specific actuators. To enhance the versatility and the efficiency of the modelling process we propose a generalized numerical algorithm based purely on geometrical inputs, which can be tailored to the desired actuator, to estimate its force-contraction profile quickly and for any combination of varying geometrical parameters. We identify a class of linearly contracting vacuum actuators that consists of a polymeric skin guided by a rigid skeleton and apply our model to two such actuators-vacuum bellows and Fluid-driven Origami-inspired Artificial Muscles-to demonstrate the versatility of our model. We perform experiments to validate that our model can predict the force profile of the actuators using its geometric principles, modularly combined with design-specific external adjustment factors. Our framework can be used as a versatile design tool that allows users to perform parametric studies and rapidly and efficiently tune actuator dimensions to produce a force-contraction profile to meet their needs, and as a pre-screening tool to obviate the need for multiple rounds of time-intensive actuator fabrication and testing.</pubmed_abstract><journal>Frontiers in robotics and AI</journal><pagination>606938</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7983108</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>A Modular Geometrical Framework for Modelling the Force-Contraction Profile of Vacuum-Powered Soft Actuators.</pubmed_title><pmcid>PMC7983108</pmcid><pubmed_authors>Gollob SD</pubmed_authors><pubmed_authors>Roche ET</pubmed_authors><pubmed_authors>Park C</pubmed_authors><pubmed_authors>Koo BHB</pubmed_authors></additional><is_claimable>false</is_claimable><name>A Modular Geometrical Framework for Modelling the Force-Contraction Profile of Vacuum-Powered Soft Actuators.</name><description>In this paper, we present a generalized modeling tool for predicting the output force profile of vacuum-powered soft actuators using a simplified geometrical approach and the principle of virtual work. Previous work has derived analytical formulas to model the force-contraction profile of specific actuators. To enhance the versatility and the efficiency of the modelling process we propose a generalized numerical algorithm based purely on geometrical inputs, which can be tailored to the desired actuator, to estimate its force-contraction profile quickly and for any combination of varying geometrical parameters. We identify a class of linearly contracting vacuum actuators that consists of a polymeric skin guided by a rigid skeleton and apply our model to two such actuators-vacuum bellows and Fluid-driven Origami-inspired Artificial Muscles-to demonstrate the versatility of our model. We perform experiments to validate that our model can predict the force profile of the actuators using its geometric principles, modularly combined with design-specific external adjustment factors. Our framework can be used as a versatile design tool that allows users to perform parametric studies and rapidly and efficiently tune actuator dimensions to produce a force-contraction profile to meet their needs, and as a pre-screening tool to obviate the need for multiple rounds of time-intensive actuator fabrication and testing.</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021</publication><modification>2025-04-20T02:31:38.075Z</modification><creation>2025-04-20T02:31:38.075Z</creation></dates><accession>S-EPMC7983108</accession><cross_references><pubmed>33763454</pubmed><doi>10.3389/frobt.2021.606938</doi></cross_references></HashMap>