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Charalampos Papakonstantinou

dblp:181/4174 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0003-3114-3330ORCID · reported

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.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%
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 › singularity handling
singularity avoidance
0.212016
Manipulator performance constraints in Cartesian admittance control for human-robot cooperation · ICRA 2016
Human-robot interaction › physical human-robot interaction
admittance control
0.212016
Manipulator performance constraints in Cartesian admittance control for human-robot cooperation · ICRA 2016
Human-robot interaction
physical human-robot interaction
0.212016
Manipulator performance constraints in Cartesian admittance control for human-robot cooperation · ICRA 2016

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

kinematic manipulability index · 0.5cartesian admittance control · 0.5
YearPublicationVenuePosition
2016 Manipulator performance constraints in Cartesian admittance control for human-robot cooperation
abstract
This paper addresses the problem of providing feedback to the operator about the manipulator's performance during human-robot physical interaction. A method is proposed that implements virtual constraints in Cartesian admittance control in order to prevent the operator from guiding the manipulator to low-performance configurations. The constraints are forces expressed in the Cartesian frame, which restrict the translation of the end-effector when the operator guides the robot below a certain performance threshold. These forces are calculated online by numerically approximating the gradient of the performance index with respect to the Cartesian frame attached to the end-effector. An experimental evaluation is conducted involving human-robot interaction with a 7-DOF LWR serial manipulator under Cartesian admittance control, using the kinematic manipulability index of the manipulator as the performance measure for singularity avoidance.
Fotios Dimeas, Vassilis C. Moulianitis, Charalampos Papakonstantinou, Nikos A. Aspragathos
ICRA3