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Gregory A. Arnold

dblp:186/3211 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1986
—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 · 87% Robot manipulation · 13%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
robot control
0.011986
Improved robot trajectory from acoustic range servo control · ICRA 1986
Robotics › Motion planning and robot control › robot control › trajectory tracking
trajectory control
0.011986
Improved robot trajectory from acoustic range servo control · ICRA 1986
Robotics › Robot manipulation
end effector
0.011986
Improved robot trajectory from acoustic range servo control · ICRA 1986

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

eddy current proximity sensor · 0.0acoustic pulse echo · 0.0
YearPublicationVenuePosition
1986 Improved robot trajectory from acoustic range servo control
abstract
A technique is described for improving the accuracy of the path followed by a robot arm in precision tracking of an object surface contour. Acoustic pulse echo data from a sensor mounted on an end effector was processed to compute distance to an object surface several inches from the sensor. Real-time path modification was then based on this computed distance. An eddy current proximity sensor positioned approximately 30 mils from the surface was used to monitor the acoustic sensor and robot performance. Both sensors produced identical results in tracking variations in distance from the surface with the robot in open loop mode for a linear path over a precision ground surface for which the robot was taught the two endpoints of the trajectory. Variation from the linear path was as large as 10 mils. With the acoustic range sensor providing input for a control algorithm to maintain a fixed offset as the robot traversed the surface, path deviation was reduced to +/- 3 mils using single measurements in the real-time mode. This is attributable to the inherent noise level of the sensor system. Averaging 10 measurements reduced the standard deviation in position measurement and control to +/- 1.2 mils.
Jeffrey S. Schoenwald, Michael S. Black, Jim F. Martin, Gregory A. Arnold, Timothy A. Allison
ICRA4