Demo Data from SIGIR 2026 / reviewers in the wild / expert
Keijo Kuusmik
dblp:181/3908
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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 · 87% Legged, aerial and field robots · 13% |
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 › motion control
depth control |
0.2 | 1 | 2016 | Depth control of the biomimetic U-CAT turtle-like AUV with experiments in real operating conditions · ICRA 2016 |
Robotics › Motion planning and robot control
robot control |
0.2 | 1 | 2016 | Depth control of the biomimetic U-CAT turtle-like AUV with experiments in real operating conditions · ICRA 2016 |
Robotics › Legged, aerial and field robots
underwater robotics |
0.1 | 1 | 2016 | Depth control of the biomimetic U-CAT turtle-like AUV with experiments in real operating conditions · ICRA 2016 |
Methods — techniques the papers use, named apart from their topics
RISE feedback control · 0.2PID control · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Depth control of the biomimetic U-CAT turtle-like AUV with experiments in real operating conditionsabstractControl of underwater vehicles is a thoroughly investigated subject but still an open problem, because of the environmental disturbances, the highly nonlinear behaviour of vehicles, the complexity of the vehicle hydrodynamics, etc. In this paper, we are interested in depth control of a bioinspired U-CAT underwater AUV in real operating conditions. Two depth control schemes are proposed, including a PID controller and a nonlinear RISE feedback controller. The proposed controllers are implemented on the robot, then tested in an open water environment. The obtained results are presented and discussed through different experimental scenarios to illustrate the efficiency of the proposed controllers, not only to successfully control the depth, but also to be robust towards external disturbances and parameters uncertainties. we conclude that RISE controller is more robust towards environmental disturbances and outperforms the PID controller when the robot is tested in real operating condition. Ahmed Chemori, Keijo Kuusmik, Taavi Salumae, Maarja Kruusmaa |
ICRA | 2 |