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Arnaud Marchadier

dblp:05/113 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2011
0000-0003-0329-2005ORCID · corroborated

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 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 · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot control
0.112005
Vision-Based Control of a Gough-Stewart Parallel Mechanism using Legs Observation · ICRA 2005
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.112005
Vision-Based Control of a Gough-Stewart Parallel Mechanism using Legs Observation · ICRA 2005
Robotics › Motion planning and robot control › robot control
parallel robot control
0.012005
Vision-Based Control of a Gough-Stewart Parallel Mechanism using Legs Observation · ICRA 2005

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

vision-based kinematics · 0.1metrological redundancy · 0.1
YearPublicationVenuePosition
2011 Comparison of Visual Registration Approaches of 3D Models for Orthodontics
Raphaël Destrez, Benjamin Albouy-Kissi, Sylvie Treuillet, Yves Lucas, Arnaud Marchadier
ACIVS5
2005 Vision-Based Control of a Gough-Stewart Parallel Mechanism using Legs Observation
abstract
This paper presents a novel approach for vision-based control of the end-effector of parallel mechanisms. It is based on the metrological redundancy paradigm, which simplifies their kinematic models by introducing additional proprioceptive sensors. By observing the mechanism legs, vision replaces advantageously these sensors by delivering, in a Cartesian frame, an exteroceptive measurement of the internal state of the mechanism. Formally, the latter is expressed by an original concept of vision-based kinematics for parallel mechanisms. Based on it, control is derived that visually servoes the direction of the legs, rather than the end-effector pose. The method is illustrated and validated on a Gough-Stewart platform simulation.
Nicolas Andreff, Arnaud Marchadier, Philippe Martinet
ICRA2