Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm
The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, h...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-01-01
|
Series: | Frontiers in Robotics and AI |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2021.790571/full |
_version_ | 1798013779759333376 |
---|---|
author | Michele Di Lecce Onaizah Onaizah Peter Lloyd James H. Chandler Pietro Valdastri |
author_facet | Michele Di Lecce Onaizah Onaizah Peter Lloyd James H. Chandler Pietro Valdastri |
author_sort | Michele Di Lecce |
collection | DOAJ |
description | The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models (Neo Hookean; Mooney–Rivlin; Yeoh; and Ogden). To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling. |
first_indexed | 2024-04-11T15:08:11Z |
format | Article |
id | doaj.art-35df609a82cc4297b7692f6503b3d752 |
institution | Directory Open Access Journal |
issn | 2296-9144 |
language | English |
last_indexed | 2024-04-11T15:08:11Z |
publishDate | 2022-01-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Robotics and AI |
spelling | doaj.art-35df609a82cc4297b7692f6503b3d7522022-12-22T04:16:44ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442022-01-01810.3389/frobt.2021.790571790571Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic AlgorithmMichele Di LecceOnaizah OnaizahPeter LloydJames H. ChandlerPietro ValdastriThe growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models (Neo Hookean; Mooney–Rivlin; Yeoh; and Ogden). To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling.https://www.frontiersin.org/articles/10.3389/frobt.2021.790571/fullsoft robots material and designmagnetic actuationhyperelastic modelsmaterial characterization and modelingevolutionary algorithminverse optimization |
spellingShingle | Michele Di Lecce Onaizah Onaizah Peter Lloyd James H. Chandler Pietro Valdastri Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm Frontiers in Robotics and AI soft robots material and design magnetic actuation hyperelastic models material characterization and modeling evolutionary algorithm inverse optimization |
title | Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm |
title_full | Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm |
title_fullStr | Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm |
title_full_unstemmed | Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm |
title_short | Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm |
title_sort | evolutionary inverse material identification bespoke characterization of soft materials using a metaheuristic algorithm |
topic | soft robots material and design magnetic actuation hyperelastic models material characterization and modeling evolutionary algorithm inverse optimization |
url | https://www.frontiersin.org/articles/10.3389/frobt.2021.790571/full |
work_keys_str_mv | AT micheledilecce evolutionaryinversematerialidentificationbespokecharacterizationofsoftmaterialsusingametaheuristicalgorithm AT onaizahonaizah evolutionaryinversematerialidentificationbespokecharacterizationofsoftmaterialsusingametaheuristicalgorithm AT peterlloyd evolutionaryinversematerialidentificationbespokecharacterizationofsoftmaterialsusingametaheuristicalgorithm AT jameshchandler evolutionaryinversematerialidentificationbespokecharacterizationofsoftmaterialsusingametaheuristicalgorithm AT pietrovaldastri evolutionaryinversematerialidentificationbespokecharacterizationofsoftmaterialsusingametaheuristicalgorithm |