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Published Online: 16 April 2019

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

Publication: Soft Robotics
Volume 6, Issue Number 2

Abstract

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.

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Information & Authors

Information

Published In

cover image Soft Robotics
Soft Robotics
Volume 6Issue Number 2April 2019
Pages: 228 - 249
PubMed: 30702390

History

Published online: 16 April 2019
Published in print: April 2019
Published ahead of print: 31 January 2019

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Department of Informatics, Centre for Robotics Research, King's College London, London, United Kingdom.
Morphological Computation and Learning Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
S.M. Hadi Sadati
Morphological Computation and Learning Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Bristol Robotics Laboratory, Faculty of Engineering, University of Bristol, Bristol, United Kingdom.
Yohan Noh
Department of Informatics, Centre for Robotics Research, King's College London, London, United Kingdom.
Jan Fraś
Centre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom.
Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland.
Ahmad Ataka
Department of Informatics, Centre for Robotics Research, King's College London, London, United Kingdom.
Centre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom.
Helge Würdemann
Department of Mechanical Engineering, University College London, London, United Kingdom.
Helmut Hauser
Bristol Robotics Laboratory, Faculty of Engineering, University of Bristol, Bristol, United Kingdom.
Ian D. Walker
Department of Electrical and Computer Engineering, Clemson University, Clemson, South Carolina.
Thrishantha Nanayakkara
Morphological Computation and Learning Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Kaspar Althoefer
Centre for Advanced Robotics @ Queen Mary (ARQ), Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom.

Notes

Address correspondence to: Ali Shiva, Department of Informatics, Centre for Robotics Research, King's College London, London WC2R 2LS, United Kingdom [email protected]

Author Disclosure Statement

No competing financial interests exist.

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