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Nan Luan

dblp:75/2530 · 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 · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
trajectory planning		 0.012001
Generation of Optimal Trajectory for Real System of an Under-actuated Manipulator · ICRA 2001
Robotics › Motion planning and robot control › robot control › underactuated systems
underactuated manipulator control		 0.012001
Generation of Optimal Trajectory for Real System of an Under-actuated Manipulator · ICRA 2001
Robotics › Motion planning and robot control › robot control
learning control		 0.012001
Generation of Optimal Trajectory for Real System of an Under-actuated Manipulator · ICRA 2001

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

optimal trajectory generation · 0.0dynamics model learning · 0.0
YearPublicationVenuePosition
2001 Generation of Optimal Trajectory for Real System of an Under-actuated Manipulator
abstract
Trajectory of an underactuated manipulator is usually generated according to both kinematics and dynamics of the manipulator, different to that of conventional manipulator. The real trajectory by a real system may differ greatly from the trajectory generated from the dynamics model because there always exist errors in the dynamic model, and the feedback control is less effective in an underactuated manipulator. A method for generation of optimal trajectory for the real system of an underactuated manipulator with nonholonomic constraints is proposed. By using this method, the dynamics model of a real system can be improved by learning, and an optimal trajectory is generated according to the model improved sequentially. The effectiveness of the method is confirmed by experiment with a golf swinging robot. The implementation and experimental results obtained of the control method are described.
Nan Luan, Aiguo Ming, Makoto Kajitani
 ICRA1