Elasticity Versus Hyperelasticity Considerations in Quasistatic Modeling of a Soft Finger-Like Robotic Appendage for Real-Time Position and Force Estimation.


Journal

Soft robotics
ISSN: 2169-5180
Titre abrégé: Soft Robot
Pays: United States
ID NLM: 101623819

Informations de publication

Date de publication:
04 2019
Historique:
pubmed: 1 2 2019
medline: 11 4 2020
entrez: 1 2 2019
Statut: ppublish

Résumé

Various methods based on hyperelastic assumptions have been developed to address the mathematical complexities of modeling motion and deformation of continuum manipulators. In this study, we propose a quasistatic approach for 3D modeling and real-time simulation of a pneumatically actuated soft continuum robotic appendage to estimate the contact force and overall pose. Our model can incorporate external load at any arbitrary point on the body and deliver positional and force propagation information along the entire backbone. In line with the proposed model, the effectiveness of elasticity versus hyperelasticity assumptions (neo-Hookean and Gent) is investigated and compared. Experiments are carried out with and without external load, and simulations are validated across a range of Young's moduli. Results show best conformity with Hooke's model for limited strains with about 6% average normalized error of position; and a mean absolute error of less than 0.08 N for force applied at the tip and on the body, demonstrating high accuracy in estimating the position and the contact force.

Identifiants

pubmed: 30702390
doi: 10.1089/soro.2018.0060
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

228-249

Auteurs

Ali Shiva (A)

1 Department of Informatics, Centre for Robotics Research, King's College London, London, United Kingdom.
2 Morphological Computation and Learning Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.

S M Hadi Sadati (SMH)

2 Morphological Computation and Learning Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
3 Bristol Robotics Laboratory, Faculty of Engineering, University of Bristol, Bristol, United Kingdom.

Yohan Noh (Y)

1 Department of Informatics, Centre for Robotics Research, King's College London, London, United Kingdom.

Jan Fraś (J)

4 Centre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom.
5 Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland.

Ahmad Ataka (A)

1 Department of Informatics, Centre for Robotics Research, King's College London, London, United Kingdom.
4 Centre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom.

Helge Würdemann (H)

6 Department of Mechanical Engineering, University College London, London, United Kingdom.

Helmut Hauser (H)

3 Bristol Robotics Laboratory, Faculty of Engineering, University of Bristol, Bristol, United Kingdom.

Ian D Walker (ID)

7 Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina.

Thrishantha Nanayakkara (T)

2 Morphological Computation and Learning Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.

Kaspar Althoefer (K)

4 Centre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom.

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Classifications MeSH