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Fabio Di Natale

dblp:320/7845 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › nonlinear control
feedback linearization
0.212022
Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots · ICRA 2022
Robotics › Motion planning and robot control › robot control › nonlinear control
gain scheduling
0.212022
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
YearPublicationVenuePosition
2022 Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots
abstract
This 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
ICRA5