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Jigien Chen

dblp:139/4896 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1987
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 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 · 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 calibration
kinematic error modeling
0.011987
Positioning error analysis for robot manipulators with all rotary joints · IEEE J. Robotics Autom. 1987
Robotics › Motion planning and robot control
robot control
0.011987
Positioning error analysis for robot manipulators with all rotary joints · IEEE J. Robotics Autom. 1987
Robotics › Motion planning and robot control › robot calibration
manipulator calibration
0.011987
Positioning error analysis for robot manipulators with all rotary joints · IEEE J. Robotics Autom. 1987

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

parameter estimation · 0.0error compensation · 0.0
YearPublicationVenuePosition
1987 Positioning error analysis for robot manipulators with all rotary joints
abstract
Advanced industrial robots are commanded to accomplish different tasks with program sequences that are executed in digital computers. The operating software within these computers provides users with information on positions and orientations of the end effectors by computing them as functions of the joint variables. These functions are generally not exact enough such that differences between the computed and the actual positions can be significant. Error sources that contribute to these differences for robots with rotary joints are examined. The effects of these errors are parameterized and measurement data are fitted to obtain the values of these parameters. It is concluded that with sufficient but not exhaustive detail in the error modeling the differences can be reduced significantly from 5.9-mm mean error with nominal model down to 0.28-mm mean error after error compensation.
Jigien Chen, Lih-Ming Chao
IEEE J. Robotics Autom.1