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R. Lewczyk

dblp:147/3059 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1984
—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 · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
adaptive control
0.011984
Adaptive path control of a manipulator with visual information · ICRA 1984
Robotics › Motion planning and robot control
robot control
0.011984
Adaptive path control of a manipulator with visual information · ICRA 1984
Robotics › Motion planning and robot control › robot control › adaptive control
self-tuning control
0.011984
Adaptive path control of a manipulator with visual information · ICRA 1984
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing
0.011984
Adaptive path control of a manipulator with visual information · ICRA 1984

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

time-series models · 0.0online parameter estimation · 0.0
YearPublicationVenuePosition
1984 Adaptive path control of a manipulator with visual information
abstract
A picture of a scene is used to extract information for an adaptive control algorithm. The object of interest is first located by means of a classifier. The position and orientation of the object are determined from a binary picture. The desired path which the gripper of the manipulator is to follow is specified by discrete points, first is the Cartesian and then in the joint coordinate system. The adaptive self-tuning controller is outlined. The controller design is based on time series difference equation model, in which the parameters are estimated on-line. The gains of the controller are tuned so that a quadratic performance criteria is minimized. The performance of the system designed is then tested experimentally. Results of these experiments are presented.
Antti J. Koivo, R. Lewczyk, T. H. Chiu
ICRA2