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J. P. Hartranft

dblp:69/2163 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, 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
2 papers
Robot navigation and mapping · 41% Robot manipulation · 41% Motion planning and robot control · 12%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
fault detection		 0.122000
Fault detection for robot manipulators with parametric uncertainty: a prediction-error-based approach · IEEE Trans. Robotics Autom. 2000
Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction Error Based Approach · ICRA 2000
Robotics › Robot manipulation
prediction error based fault detection		 0.122000
Fault detection for robot manipulators with parametric uncertainty: a prediction-error-based approach · IEEE Trans. Robotics Autom. 2000
Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction Error Based Approach · ICRA 2000
Robotics › Motion planning and robot control › robot control
adaptive control		 0.022000
Fault detection for robot manipulators with parametric uncertainty: a prediction-error-based approach · IEEE Trans. Robotics Autom. 2000
Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction Error Based Approach · ICRA 2000
Machine learning › Trustworthy machine learning › uncertainty estimation › model uncertainty
parameter uncertainty		 0.012000
Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction Error Based Approach · ICRA 2000

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

filtered torque prediction error · 0.1adaptive estimation · 0.0adaptive algorithm · 0.0
YearPublicationVenuePosition
2000 Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction Error Based Approach
abstract
We introduce a new approach to fault detection for robot manipulators. The technique, which is based on the isolation of fault signatures via filtered torque prediction error estimates, does not require measurements or estimates of manipulator acceleration as is the case with some of the previously suggested methods. The method is formally demonstrated to be robust under uncertainty in the robot parameters. Furthermore, an adaptive version of the algorithm is introduced, and shown to both improve coverage and significantly reduce detection times. The effectiveness of the approach is demonstrated by experiments with a two-joint manipulator system.
Warren E. Dixon, Ian D. Walker, Darren M. Dawson, J. P. Hartranft
 ICRA4
2000 Fault detection for robot manipulators with parametric uncertainty: a prediction-error-based approach
abstract
In this paper, we introduce a new approach to fault detection for robot manipulators. The technique, which is based on the isolation of fault signatures via filtered torque prediction error estimates, does not require measurements or estimates of manipulator acceleration as is the case with some previously suggested methods. The method is formally demonstrated to be robust under uncertainty in the robot parameters. Furthermore, an adaptive version of the algorithm is introduced, and shown to both improve coverage and significantly reduce detection times. The effectiveness of the approach is demonstrated by experiments with a two-joint manipulator system.
Warren E. Dixon, Ian D. Walker, Darren M. Dawson, J. P. Hartranft
 IEEE Trans. Robotics Autom.4