Demo Data from VLDB 2026 / reviewers in the wild / expert
Fabio Di Natale
dblp:320/7845
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021
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 |
Legged, aerial and field robots · 62% Motion planning and robot control · 38% |
Topics — the 2 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 › nonlinear control
feedback linearization |
0.2 | 1 | 2022 | Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots · ICRA 2022 |
Robotics › Motion planning and robot control › robot control › nonlinear control
gain scheduling |
0.2 | 1 | 2022 | Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
gain scheduling · 0.6feedback linearization · 0.6computational fluid dynamics · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Centroidal Aerodynamic Modeling and Control of Flying Multibody RobotsabstractThis paper presents a modeling and control frame-work for multibody flying robots subject to non-negligible aero-dynamic forces acting on the centroidal dynamics. First, aero-dynamic forces are calculated during robot flight in different operating conditions by means of Computational Fluid Dynamics (CFD) analysis. Then, analytical models of the aerodynamics coefficients are generated from the dataset collected with CFD analysis. The obtained simplified aerodynamic model is also used to improve the flying robot control design. We present two control strategies: compensating for the aerodynamic effects via feedback linearization and enforcing the controller robustness with gain-scheduling. Simulation results on the jet-powered humanoid robot iRonCub validate the proposed approach. Tong Hui, Antonello Paolino, Gabriele Nava, Giuseppe L'Erario, Fabio Di Natale, Fabio Bergonti, Francesco Braghin, Daniele Pucci |
ICRA | 5 |