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Andrew C. Clegg

dblp:03/4880 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 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 · 87% Robot manipulation · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › compliant motion control
hybrid position/force control
0.012001
Self-tuning Position and Force Control of an Underwater Manipulator · ICRA 2001
Robotics › Motion planning and robot control › manipulator control
underwater manipulator control
0.012001
Self-tuning Position and Force Control of an Underwater Manipulator · ICRA 2001

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

pole placement · 0.0adaptive self-tuning control · 0.0
YearPublicationVenuePosition
2001 Self-tuning Position and Force Control of an Underwater Manipulator
abstract
Current generation unmanned underwater vehicles, equipped with robotic manipulators, are teleoperated and consequently place a large workload burden on the human operator. A greater degree of automation could improve the efficiency and accuracy with which underwater tasks are carried out. These tasks can involve manipulator motion that is both unconstrained and/or constrained. For unconstrained motion, where a trajectory requires following, a prerequisite is good joint angle control. An adaptive self-tuning pole-placement controller is used for joint angle control. Practical results show the benefits compared to the conventional fixed-gain control. For constrained motion, simultaneous controls of position and force are often required. An adaptive hybrid position/force controller is proposed and compared to a fixed-gain version. Simulation and practical results illustrate the merits and drawbacks of each scheme.
Andrew C. Clegg, Matthew W. Dunnigan, David M. Lane
ICRA1