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Marc Becquet

dblp:139/7227 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1991
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 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 · 50% 3D vision · 25% Optimization for machine learning · 25%

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

TopicWeightPapersLastEvidence papers
Machine learning › Optimization for machine learning
error compensation
0.011991
Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991
Computer vision › 3D vision › geometric estimation
geometric parameter estimation
0.011991
Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991
Robotics › Motion planning and robot control › robot calibration
kinematic calibration
0.011991
Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991
Robotics › Motion planning and robot control › robot control › motion control
robot positioning
0.011991
Kinematic calibration and geometrical parameter identification for robots · IEEE Trans. Robotics Autom. 1991

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

maximum likelihood estimation · 0.0
YearPublicationVenuePosition
1991 Kinematic calibration and geometrical parameter identification for robots
abstract
A technique is presented for the calibration of robots based on a maximum-likelihood approach for the identification of geometrical errors. A new experimental setup is presented for measurement of the end-reflector position errors. The errors of position and orientation of the measuring device are included in the algorithm and identified. Tests have been carried out on a robot with six degrees of freedom. Tests show that this technique can reduce the mean error distance by a factor of more than 15. Compensation algorithms are presented, based on the improved knowledge of the geometrical model.>
Jean-Michel Renders, Eric Rossignol, Marc Becquet, Raymond Hanus
IEEE Trans. Robotics Autom.3