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Martin Prüfer

dblp:03/5195 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1994
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

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 · 62% Probabilistic and Bayesian machine learning · 38%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › system identification
robot dynamics identification
0.011994
Identification of Robot Dynamics with Differential and Integral Models: A Comparison · ICRA 1994
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › regression
least squares regression
0.011994
Identification of Robot Dynamics with Differential and Integral Models: A Comparison · ICRA 1994
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
parameter estimation
0.011994
Identification of Robot Dynamics with Differential and Integral Models: A Comparison · ICRA 1994

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

signal differentiation · 0.0least-squares estimation · 0.0
YearPublicationVenuePosition
1994 Identification of Robot Dynamics with Differential and Integral Models: A Comparison
abstract
Compares the integral model suggested by Gautier and Khalil (1988, 1989) for identification of robot dynamics with the differential equations of the robot. The advantage of identification by the integral model is, that no measurement of acceleration is required. Although appealing at a first glance, this approach yields problems in conjunction with the necessary least squares algorithm for parameter estimation. The authors even succeed in getting better results by employing the differential equations, without measurement of acceleration. It is shown, how to achieve reasonable good results with low noise by just derivating the position signals. Finally, the authors' results are illustrated by experiments with a direct driven two link robot.>
Martin Prüfer, Friedrich M. Wahl
ICRA1