Demo Data from EDBT 2026 / reviewers in the wild / expert
Kaushik Nagarajan
dblp:72/10695
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 · 100% |
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 › sensor-based control › visual servoing
field of view constraint |
0.1 | 1 | 2011 | Keeping Multiple Moving Targets in the Field of View of a Mobile Camera · IEEE Trans. Robotics 2011 |
Robotics › Motion planning and robot control › robot control › sensor-based control
visual servoing |
0.1 | 1 | 2011 | Keeping Multiple Moving Targets in the Field of View of a Mobile Camera · IEEE Trans. Robotics 2011 |
Robotics › Motion planning and robot control › manipulator control
task function control |
0.0 | 1 | 2011 | Keeping Multiple Moving Targets in the Field of View of a Mobile Camera · IEEE Trans. Robotics 2011 |
Methods — techniques the papers use, named apart from their topics
lyapunov stability analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Keeping Multiple Moving Targets in the Field of View of a Mobile CameraabstractThis study introduces a novel visual servo controller that is designed to control the pose of the camera to keep multiple objects in the field of view (FOV) of a mobile camera. In contrast with other visual servo methods, the control objective is not formulated in terms of a goal pose or a goal image. Rather, a set of underdetermined task functions are developed to regulate the mean and variance of a set of image features. Regulating these task functions inhibits feature points from leaving the camera FOV. An additional task function is used to maintain a high level of motion perceptibility, which ensures that desired feature point velocities can be achieved. These task functions are mapped to camera velocity, which serves as the system input. A proof of stability is presented for tracking three or fewer targets. Experiments of tracking eight or more targets have verified the performance of the proposed method. Nicholas R. Gans, Guoqiang Hu 0001, Kaushik Nagarajan, Warren E. Dixon |
IEEE Trans. Robotics | 3 |