A framework for sensorless identification
of needle-tissue interaction forces in robot-assisted biopsies

Marco Ferro; Claudio Gaz; Marilena Vendittelli

A framework for sensorless identification of needle-tissue interaction forces in robot-assisted biopsies

Marco Ferro, Claudio Gaz, Marilena Vendittelli

Research output: Contribution to conference › Paper › peer-review

Abstract

Abstract— During teleoperated robot-assisted biopsies, force and visual feedback are fundamental to guarantee accuracy and safety in executing the task. To allow seamless introduction of the robot in the clinical flow, it is desirable to measure interaction forces without relying on dedicated, additional sensors. On the other hand, typical imaging systems do not offer a real-time 3D view of the remote site at the operator-side. In this document, we extend a previous work with two contributions: i) a sensorless needle-tissue interaction model identification algorithm; ii) a modular framework, interfaced with a virtual environment, offering a complete visualization of the procedure at the operator-side. The identification algorithm is validated on an isinglass-based phantom, while the framework is tested through the CoppeliaSim (former V-REP) software.

Original language	English
Publication status	Published - 4 Jun 2020
Event	2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 Workshop : Shared Autonomy : Learning and Control (SALC) - Paris, France (Held online) Duration: 31 May 2020 → 31 Aug 2020

Conference

Conference	2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 Workshop : Shared Autonomy : Learning and Control (SALC)
Period	31/05/20 → 31/08/20

Bibliographical note

Organising Body: IEEE Robotics and Automation Society

Keywords

Computer science and informatics

Cite this

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title = "A framework for sensorless identification of needle-tissue interaction forces in robot-assisted biopsies",

abstract = "Abstract— During teleoperated robot-assisted biopsies, force and visual feedback are fundamental to guarantee accuracy and safety in executing the task. To allow seamless introduction of the robot in the clinical flow, it is desirable to measure interaction forces without relying on dedicated, additional sensors. On the other hand, typical imaging systems do not offer a real-time 3D view of the remote site at the operator-side. In this document, we extend a previous work with two contributions: i) a sensorless needle-tissue interaction model identification algorithm; ii) a modular framework, interfaced with a virtual environment, offering a complete visualization of the procedure at the operator-side. The identification algorithm is validated on an isinglass-based phantom, while the framework is tested through the CoppeliaSim (former V-REP) software.",

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A framework for sensorless identification of needle-tissue interaction forces in robot-assisted biopsies. / Ferro, Marco; Gaz, Claudio; Vendittelli, Marilena.
2020. Paper presented at 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 Workshop : Shared Autonomy : Learning and Control (SALC).

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - A framework for sensorless identification of needle-tissue interaction forces in robot-assisted biopsies

AU - Ferro, Marco

AU - Gaz, Claudio

AU - Vendittelli, Marilena

N1 - Organising Body: IEEE Robotics and Automation Society

PY - 2020/6/4

Y1 - 2020/6/4

N2 - Abstract— During teleoperated robot-assisted biopsies, force and visual feedback are fundamental to guarantee accuracy and safety in executing the task. To allow seamless introduction of the robot in the clinical flow, it is desirable to measure interaction forces without relying on dedicated, additional sensors. On the other hand, typical imaging systems do not offer a real-time 3D view of the remote site at the operator-side. In this document, we extend a previous work with two contributions: i) a sensorless needle-tissue interaction model identification algorithm; ii) a modular framework, interfaced with a virtual environment, offering a complete visualization of the procedure at the operator-side. The identification algorithm is validated on an isinglass-based phantom, while the framework is tested through the CoppeliaSim (former V-REP) software.

AB - Abstract— During teleoperated robot-assisted biopsies, force and visual feedback are fundamental to guarantee accuracy and safety in executing the task. To allow seamless introduction of the robot in the clinical flow, it is desirable to measure interaction forces without relying on dedicated, additional sensors. On the other hand, typical imaging systems do not offer a real-time 3D view of the remote site at the operator-side. In this document, we extend a previous work with two contributions: i) a sensorless needle-tissue interaction model identification algorithm; ii) a modular framework, interfaced with a virtual environment, offering a complete visualization of the procedure at the operator-side. The identification algorithm is validated on an isinglass-based phantom, while the framework is tested through the CoppeliaSim (former V-REP) software.

KW - Computer science and informatics

UR - https://members.loria.fr/SIvaldi/wp-content/blogs.dir/70/files/sites/70/2020/05/SALC2020_paper_15c.pdf

M3 - Paper

T2 - 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020 Workshop : Shared Autonomy : Learning and Control (SALC)

Y2 - 31 May 2020 through 31 August 2020

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