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Francesco Taurisano

dblp:29/229 · 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
Motion planning and robot control · 44% Planning, search and constraint satisfaction · 44% Robot manipulation · 13%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › intelligent control
fuzzy control		 0.011999
Visual Servoing of a Robotic Manipulator Based on Fuzzy Logic Control · ICRA 1999
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing		 0.011999
Visual Servoing of a Robotic Manipulator Based on Fuzzy Logic Control · ICRA 1999
Robotics › Robot manipulation › grasping
target-driven grasping		 0.011999
Visual Servoing of a Robotic Manipulator Based on Fuzzy Logic Control · ICRA 1999

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

vision algorithm · 0.0supervised rule construction · 0.0fuzzy logic · 0.0
YearPublicationVenuePosition
1999 Visual Servoing of a Robotic Manipulator Based on Fuzzy Logic Control
abstract
In this paper a solution to a manipulation control problem (target identification and grasping) using a real platform in combination with a vision system is proposed. The task for the end-effector is to approach a randomly placed spherical object of known size. The control law is approximated by using an approach based on fuzzy logic. The controller determines the parameters of the target (a spherical object acquired from the vision system mounted on the manipulator) using a vision algorithm. The fuzzy rules are built in a supervised approach after studying the behavior of the system. Experimental results obtained using an industrial manipulator (PUMA 560 with 6-DOF) are presented.
R. De Guiseppe, Francesco Taurisano, Cosimo Distante, Alfredo Anglani
 ICRA2