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Online prediction of needle shape deformation in moving soft tissues from visual feedback

Jason Chevrie 1 Alexandre Krupa 1 Marie Babel 1
1 Lagadic - Visual servoing in robotics, computer vision, and augmented reality
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : — With the increasing number of clinical interventions using needle shaped tools, robotic control of needle insertion procedures has been an active research field for many years. In this work we propose a 3D model of a flexible needle that takes into account tissue deformations in order to predict the needle shape and trajectory when it is inserted using a robotic arm. To account for tissue displacements, we designed a method based on visual feedback that updates the interaction model between the needle and the tissue using an unscented Kalman filter. Results obtained from several needle insertions in a soft tissue phantom showed that the method gives good performance in terms of needle trajectory prediction. This model was also considered in a closed-loop control approach to allow automatic reaching of a target.
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  • HAL Id : hal-01355484, version 1

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Jason Chevrie, Alexandre Krupa, Marie Babel. Online prediction of needle shape deformation in moving soft tissues from visual feedback. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS'16, Oct 2016, Daejeon, South Korea. pp.2357-2362. ⟨hal-01355484⟩

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