Demo Data from EDBT 2026 / reviewers in the wild / expert
Daniel Viegas
dblp:75/11058
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2012
0000-0001-5847-3095ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Robot navigation and mapping · 75% Motion planning and robot control · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
localization |
0.1 | 1 | 2012 | Position and velocity filters for intervention AUVs based on single range and depth measurements · ICRA 2012 |
Robotics › Robot navigation and mapping
multi-robot navigation |
0.1 | 1 | 2012 | Position and velocity filters for intervention AUVs based on single range and depth measurements · ICRA 2012 |
Robotics › Motion planning and robot control
observer design |
0.1 | 1 | 2012 | Position and velocity filters for intervention AUVs based on single range and depth measurements · ICRA 2012 |
Robotics › Robot navigation and mapping
state estimation |
0.1 | 1 | 2012 | Position and velocity filters for intervention AUVs based on single range and depth measurements · ICRA 2012 |
Methods — techniques the papers use, named apart from their topics
state observer · 0.1range measurements · 0.1observability analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Position and velocity filters for intervention AUVs based on single range and depth measurementsabstractThis paper proposes novel cooperative navigation solutions for an Intervention Autonomous Underwater Vehicle (I-AUV) working in tandem with an Autonomous Surface Craft (ASC). The I-AUV is assumed to be moving in the presence of constant unknown ocean currents, and aims to estimate its position relying on measurements of its range to the ASC and of its depth relatively to the sea level. Two different scenarios are considered: in one, the ASC transmits its position and velocity to the I-AUV, while in the other the ASC transmits only its position, and the I-AUV has access to measurements of its velocity relative to the ASC. A sufficient condition for observability and a method for designing state observers with Globally Asymptotically Stable (GAS) error dynamics are presented for both problems. Finally, simulation results are included and discussed to assess the performance of the proposed solutions in the presence of measurement noise. Daniel Viegas, Pedro Tiago Martins Batista, Paulo Oliveira 0001, Carlos Silvestre |
ICRA | 1 |