AUTOMED 2021

Title: Robotic needle steering in deformable tissues with extreme learning machines

Authors: Pedro Henrique Suruagy Perrusi, Anna Cazzaniga, Paul Baksic, Eleonora Tagliabue, Elena De Momi, Hadrien Courtecuisse

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.

Accepted to AUTOMED 2021

Open access paper: https://arxiv.org/pdf/2104.06510 ICube AVR Laboratory webpage: https://avr.icube.unistra.fr/index.php/Accueil

MIMESIS Research team webpage: https://mimesis.inria.fr/ NEARLAB webpage: https://nearlab.polimi.it/ ALTAIR Robotics webpage: https://metropolis.scienze.univr.it/

Project webpage: https://hadrien.courtecuisse.cnrs.fr/

Acknowledgement: This work was supported by French National Research Agency (ANR) within the project SPERRY ANR-18-CE33-0007 and the Investissements d’Avenir program (ANR-11-LABX-0004, Labex CAMI).