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Paul Baksic

dblp:274/9166 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 1 since 2021Systems, architecture and hardware · 2 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Robot manipulation · 83% Motion planning and robot control · 17%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Medical and health informatics · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › medical robotics
needle insertion
0.922021
Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021
Robotic needle insertion in moving soft tissues using constraint-based inverse Finite Element simulation · ICRA 2020
Robotics › Robot manipulation › medical robotics
needle steering
0.922021
Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021
Robotic needle insertion in moving soft tissues using constraint-based inverse Finite Element simulation · ICRA 2020
Robotics › Robot manipulation › medical robotics
physiological motion compensation
0.512021
Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021
Robotics › Motion planning and robot control › teleoperation
shared control
0.512021
Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation · ICRA 2021

Methods — techniques the papers use, named apart from their topics

inverse finite element simulation · 1.0closed-loop control · 1.0inverse problem solving · 0.9finite element simulation · 0.9
YearPublicationVenuePosition
2021 Shared control strategy for needle insertion into deformable tissue using inverse Finite Element simulation
abstract
This paper deals with the problem of needle steering in deformable tissues subject to physiological motions. A novel shared control method is proposed, which combines an automatic needle steering algorithm with the motions applied by the radiologist, in order to place the needle tip at the desired location. The core motivation is to leave potentially dangerous decisions and actions to the practitioner, whereas complex non-intuitive manipulations of the needle are performed automatically, in particular to compensate for breathing motions. The most original part of the present work lies in the method used to combine user inputs with a closed-loop automatic needle steering control method based on inverse Finite Element simulations. The method is evaluated with a realistic virtual environment using 2D X-ray projection images. The results are compared with those obtained with a fully teleoperated system, on the one hand, and with a fully automatic solution, on the other hand. These experiments show that the shared control solution allows for a better needle tip placement when only projection imaging is available.
Paul Baksic, Hadrien Courtecuisse, Bernard Bayle
ICRA1
2020 Robotic needle insertion in moving soft tissues using constraint-based inverse Finite Element simulation
abstract
This paper introduces a method for robotic steering of a flexible needle inside moving and deformable tissues. The method relies on a set of objective functions allowing to automatically steer the needle along a predefined path. In order to follow the desired trajectory, an inverse problem linking the motion of the robot end effector with the objective functions is solved using a Finite Element simulation. The main contribution of the article is the new constraint-based formulation of the objective functions allowing to: 1) significantly reduce the computation time; 2) increase the accuracy and stability of the simulation-guided needle insertion. The method is illustrated, and its performances are characterized in a realistic framework, using a direct simulation of the respiratory motion generated from in vivo data of a pig. Despite the highly non-linear behavior of the numerical simulation and the significant deformations occurring during the insertion, the obtained performances enable the possibility to follow the trajectory with the desired accuracy for medical purpose.
Paul Baksic, Hadrien Courtecuisse, Christian Duriez, Bernard Bayle
ICRA1