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Robotic needle steering in deformable tissues with extreme learning machines

Abstract : Control strategies for robotic needle steering in soft tissues must account for complex interactions between the needle and the tissue to achieve accurate needle tip positioning. Recent findings show faster robotic command rate can improve the control stability in realistic scenarios. This study proposes the use of Extreme Learning Machines to provide fast commands for robotic needle steering. A synthetic dataset based on the inverse finite element simulation control framework is used to train the model. Results show the model is capable to infer commands 66% faster than the inverse simulation and reaches acceptable precision even on previously unseen trajectories.
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https://hal.archives-ouvertes.fr/hal-03187930
Contributor : Pedro Henrique Suruagy Perrusi <>
Submitted on : Thursday, April 1, 2021 - 6:08:48 PM
Last modification on : Wednesday, April 21, 2021 - 10:09:28 AM

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  • HAL Id : hal-03187930, version 1
  • ARXIV : 2104.06510

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Pedro Henrique Suruagy Perrusi, Anna Cazzaniga, Paul Baksic, Eleonora Tagliabue, Elena de Momi, et al.. Robotic needle steering in deformable tissues with extreme learning machines. AUTOMED 2021, Jun 2021, Basel, Switzerland. ⟨hal-03187930⟩

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