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Suryarghya Chakraborti

dblp:63/6996 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2007
—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 · 67% Multi-agent systems · 33%

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
behavior-based control
0.112007
Experiences of formation control of multi-robot systems with the Null-Space-based Behavioral Control · ICRA 2007
Knowledge, reasoning and agents › Multi-agent systems
formation control
0.112007
Experiences of formation control of multi-robot systems with the Null-Space-based Behavioral Control · ICRA 2007
Robotics › Motion planning and robot control
multi-robot control
0.112007
Experiences of formation control of multi-robot systems with the Null-Space-based Behavioral Control · ICRA 2007

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

inverse kinematics · 0.1behavior-based control · 0.1
YearPublicationVenuePosition
2007 Experiences of formation control of multi-robot systems with the Null-Space-based Behavioral Control
abstract
In this paper, two experimental case studies performed with a multi-robot system made of 6 Khepera II mobile robots are presented. The experiments, performed at the laboratory LAI (Laboratorio di Automazione Industriale) of the Universita degli Studi di Cassino, are aimed at testing the performances and the robustness of a behavior-based technique, namely the null-space-based behavioral control (NSB), while executing different kinds of missions. In particular, the NSB approach, based on an inverse kinematic technique inherited by industrial manipulator applications, has been developed to control a generic team of autonomous vehicles and it has been implemented on a centralized architecture to control, at a kinematic level, a platoon of autonomous mobile robots.
Gianluca Antonelli, Filippo Arrichiello, Suryarghya Chakraborti, Stefano Chiaverini
ICRA3