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Elena P. Moreno

dblp:383/4634 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—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
Motion planning and robot control · 64% Legged, aerial and field robots · 36%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots
0.812024
Flight Validation of a Global Singularity-Free Aerodynamic Model for Flight Control of Tail Sitters · ICRA 2024
Robotics › Motion planning and robot control › robot control
optimization-based control
0.812024
Flight Validation of a Global Singularity-Free Aerodynamic Model for Flight Control of Tail Sitters · ICRA 2024
Robotics › Motion planning and robot control
robot control
0.812024
Flight Validation of a Global Singularity-Free Aerodynamic Model for Flight Control of Tail Sitters · ICRA 2024
Robotics › Legged, aerial and field robots
aerodynamic modeling
0.212024
Flight Validation of a Global Singularity-Free Aerodynamic Model for Flight Control of Tail Sitters · ICRA 2024
Robotics › Motion planning and robot control › robot control
flight control
0.212024
Flight Validation of a Global Singularity-Free Aerodynamic Model for Flight Control of Tail Sitters · ICRA 2024

Methods — techniques the papers use, named apart from their topics

semidefinite programming · 0.8polynomial optimization · 0.8
YearPublicationVenuePosition
2024 Flight Validation of a Global Singularity-Free Aerodynamic Model for Flight Control of Tail Sitters
abstract
This work validates through flight tests a previously developed wide-envelope singularity-free aerodynamic framework, called ϕ-theory, for modeling dual-engine tail-sitting flying-wing vehicles for optimization-based control. The ϕ-theory methodology imposes a specific geometry on aerodynamic coefficients that leads to polynomial differential equations of motion amenable to semidefinite programming optimization. Through ϕ-theory, we illustrate a typical predicted longitudinal and lateral flight envelope of a tail-sitting vehicle, which, while commonplace for fixed-wing aircraft in performance textbooks, is a novel figure that generalizes fixed-wing doghouse plots to tail-sitting vehicles. This flight envelope figure suggests a novel, natural and intuitive remote piloting interface that we validate in flight tests. Furthermore, we further validate ϕ-theory through the computation of flight features in simulation and their subsequent observation in flight tests.
Krishna Murali, Elena P. Moreno, Leandro R. Lustosa
ICRA2