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Jagadesh Manivannan

dblp:246/7720 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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
1 paper
Motion planning and robot control · 87% Robot manipulation · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot kinematics
manipulability optimization
0.412019
Manipulability Optimization Control of a Serial Redundant Robot for Robot-assisted Minimally Invasive Surgery · ICRA 2019
Robotics › Motion planning and robot control
robot control
0.412019
Manipulability Optimization Control of a Serial Redundant Robot for Robot-assisted Minimally Invasive Surgery · ICRA 2019
Robotics › Robot manipulation › medical robotics › surgical robotics
minimally invasive surgery
0.112019
Manipulability Optimization Control of a Serial Redundant Robot for Robot-assisted Minimally Invasive Surgery · ICRA 2019

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

operational space formulation · 0.4null-space control · 0.4impedance control · 0.4
YearPublicationVenuePosition
2019 Manipulability Optimization Control of a Serial Redundant Robot for Robot-assisted Minimally Invasive Surgery
abstract
This paper proposes a manipulability optimization control of a 7-DoF robot manipulator for Robot-Assisted Minimally Invasive Surgery (RAMIS), which at the same time guarantees a Remote Center of Motion (RCM). The first degree of redundancy of the manipulator is used to achieve an RCM constraint, the second one is adopted for manipulability optimization. A hierarchical operational space formulation is introduced to integrate all the control components, including a Cartesian compliance control involving the main surgical task, a first null-space controller for the RCM constraint, and a second null-space controller for manipulability optimization. Experiments with virtual surgical tasks, in an augmented reality environment, were performed to validate the proposed control strategy using the KUKA LWR 4 +. The results demonstrate that end-effector accuracy and RCM constraint can be guaranteed, along with improving the manipulability of the surgical tip.
Hang Su 0001, Jagadesh Manivannan, Luca Bascetta, Giancarlo Ferrigno, Elena De Momi
ICRA3