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L. G. van Willigenburg

dblp:92/3593 · also Gerard van Willigenburg · DBLP profile ↗
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1ranked-venue papers
0as 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 · 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%
Theoretical computer science
1 paper
Mathematical optimization · 100%

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		 0.012001
A New Optimization Algorithm for Singular and Non-Singular Digital Time-optimal Control of Robots · ICRA 2001
Robotics › Motion planning and robot control › robot control › optimal control
time-optimal control		 0.012001
A New Optimization Algorithm for Singular and Non-Singular Digital Time-optimal Control of Robots · ICRA 2001
Mathematical optimization › control theory
trajectory optimization		 0.012001
A New Optimization Algorithm for Singular and Non-Singular Digital Time-optimal Control of Robots · ICRA 2001

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

gauss-newton method · 0.1conjugate gradient · 0.1
YearPublicationVenuePosition
2001 A New Optimization Algorithm for Singular and Non-Singular Digital Time-optimal Control of Robots
abstract
Time-optimal controls for 2-link robots are often of bang-bang type. Many algorithms to solve time-optimal robot control problems a-priori assume the optimal control to be bang-bang. Industrial robots very often have 5 or 6 links and then the associated time-optimal controls are usually singular. This paper presents a new algorithm that enables computation of both bang-bang and singular time-optimal controls for robots. The algorithm uses both the conjugate gradient and Gauss-Newton method to enhance its efficiency and does not require state-parameterization, which introduces additional errors. The algorithm is used to compute time-optimal controls for an industrial 5-link robot model including gravity and viscous friction.
Camile W. J. Hol, L. G. van Willigenburg, Eldert J. van Henten, Gerrit van Straten
 ICRA2