Demo Data from VLDB 2026 / reviewers in the wild / expert
Suryarghya Chakraborti
dblp:63/6996
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control
behavior-based control |
0.1 | 1 | 2007 | 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 |
0.1 | 1 | 2007 | 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 |
0.1 | 1 | 2007 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Experiences of formation control of multi-robot systems with the Null-Space-based Behavioral ControlabstractIn 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 |
ICRA | 3 |