000 02640 am a22003133u 4500
042 _adc
100 1 0 _aWu, Zicong
_eauthor
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700 1 0 _aDe Iturrate Reyzabal, Mikel
_eauthor
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700 1 0 _aSadati, S.M.Hadi
_eauthor
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700 1 0 _aLiu, Hongbin
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700 1 0 _aOurselin, Sebastien
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700 1 0 _aLeff, Daniel
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700 1 0 _aKatzschmann, Robert K.
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700 1 0 _aRhode, Kawal
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700 1 0 _aBergeles, Christos
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245 0 0 _aTowards A Physics-based Model for Steerable Eversion Growing Robots
260 _c2023-02.
500 _a/pmc/articles/PMC7614130/
500 _a/pubmed/36733442
520 _aSoft robots that grow through eversion/apical extension can effectively navigate fragile environments such as ducts and vessels inside the human body. This paper presents the physics-based model of a miniature steerable eversion growing robot. We demonstrate the robot's growing, steering, stiffening and interaction capabilities. The interaction between two robot-internal components is explored, i.e., a steerable catheter for robot tip orientation, and a growing sheath for robot elongation/retraction. The behavior of the growing robot under different inner pressures and external tip forces is investigated. Simulations are carried out within the SOFA framework. Extensive experimentation with a physical robot setup demonstrates agreement with the simulations. The comparison demonstrates a mean absolute error of 10 - 20% between simulation and experimental results for curvature values, including catheter-only experiments, sheath-only experiments and full system experiments. To our knowledge, this is the first work to explore physics-based modelling of a tendon-driven steerable eversion growing robot. While our work is motivated by early breast cancer detection through mammary duct inspection and uses our MAMMOBOT robot prototype, our approach is general and relevant to similar growing robots.
540 _a
540 _ahttps://creativecommons.org/licenses/by/4.0/This work is licensed under a CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license.
540 _ahttps://creativecommons.org/licenses/by/4.0/open access, the author has applied for a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. https://creativecommons.org/licenses/by/4.0/
546 _aen
690 _aArticle
655 7 _aText
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786 0 _nIEEE Robot Autom Lett
856 4 1 _uhttp://dx.doi.org/10.1109/LRA.2023.3234823
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