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Stephan Voss

dblp:89/1509 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1999
—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
Robot manipulation · 61% Motion planning and robot control · 39%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
grasping
0.011999
Planning of Regrasp Operations · ICRA 1999
Robotics › Motion planning and robot control
motion planning
0.011999
Planning of Regrasp Operations · ICRA 1999
Robotics › Robot manipulation › grasping › grasp planning
regrasp planning
0.011999
Planning of Regrasp Operations · ICRA 1999
Robotics › Motion planning and robot control › robot kinematics
kinematic constraints
0.011999
Planning of Regrasp Operations · ICRA 1999

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

search through compatible triples · 0.0offline-online computation subdivision · 0.0
YearPublicationVenuePosition
1999 Planning of Regrasp Operations
abstract
Robotic manipulators in contemporary work-cells are often incapable of solving even simple pick-and-place operations. More specifically, most systems require each object to be supplied in the same and pre-defined way. Introducing regrasping to the production cycle relaxes the constraints imposed on supply mechanisms. Regrasping describes the operation that must be performed whenever an object's pick-up grasp is incompatible with its put-down grasp. The paper presents a novel approach to solve the regrasp problem for robots equipped with parallel-jaw end-effecters. The proposed method first evaluates the object's possible grasps and placements. These are subsequently combined to form grasp-placement-grasp triples. A regrasp sequence leading from the pick-up to the put-down grasp is generated by searching through the resulting space of compatible grasp-placement-grasp triples. The algorithm takes kinematic and geometric constraints of both the manipulator and the objects into account. Computational concerns are addressed by subdividing the calculation into an off-line and a fast online phase.
Sascha Stoeter, Stephan Voss, Nikolaos Papanikolopoulos, Heiko Mosemann
ICRA2