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Patrick Stelzer

dblp:177/4616 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—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 · 44% Optimization for machine learning · 44% Robot manipulation · 13%

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

TopicWeightPapersLastEvidence papers
Machine learning › Optimization for machine learning
error compensation
0.212016
High accurate robotic drilling with external sensor and compliance model-based compensation · ICRA 2016
Robotics › Motion planning and robot control
robot control
0.212016
High accurate robotic drilling with external sensor and compliance model-based compensation · ICRA 2016
Robotics › Robot manipulation › industrial robot
drilling robot
0.112016
High accurate robotic drilling with external sensor and compliance model-based compensation · ICRA 2016

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

external sensor-based compensation · 0.2compliance model-based compensation · 0.2
YearPublicationVenuePosition
2016 High accurate robotic drilling with external sensor and compliance model-based compensation
abstract
High accurate absolute robot positioning is a requirement, and still a challenge, in many applications, such as drilling in the aerospace industry. The accuracy is affected due to many sources of errors from robot model, tool calibration, sensor and product uncertainties. While model-based error compensation cannot reach the desired accuracy, sensor-based compensation appears as the practical solution to increase the robot positioning accuracy. A structured analysis of the error sources in robotic manufacturing processes can facilitate error identification and further compensation. This paper describes an error source breaking down approach for analyzing robotic manufacturing processes. Moreover, an external sensor-based compensation is proposed for error reduction and error identification. Comparison with a compliance model-based compensation is performed. The proposed approach is applied to a robotic drilling process for aircraft manufacturing, considered a general and real industrial application. Further validation through experimentation is performed. The validation revealed a clear improvement in robot positioning accuracy and the benefits of the proposed error source structure for analysis.
Julian Ricardo Diaz Posada, Ulrich Schneider, Sergej Pidan, Milad Geravand, Patrick Stelzer, Alexander Verl
ICRA5