EDBT 2026 Demo / reviewers in the wild / expert
Roel Cano
dblp:181/4000
· 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 · 70% Legged, aerial and field robots · 30% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots
aerial robots |
0.2 | 1 | 2016 | Minimum-energy path generation for a quadrotor UAV · ICRA 2016 |
Robotics › Motion planning and robot control › trajectory optimization
minimum-energy trajectory |
0.2 | 1 | 2016 | Minimum-energy path generation for a quadrotor UAV · ICRA 2016 |
Robotics › Motion planning and robot control › trajectory planning
quadrotor trajectory planning |
0.2 | 1 | 2016 | Minimum-energy path generation for a quadrotor UAV · ICRA 2016 |
Robotics › Motion planning and robot control
trajectory optimization |
0.2 | 1 | 2016 | Minimum-energy path generation for a quadrotor UAV · ICRA 2016 |
Robotics › Legged, aerial and field robots › aerial robots › UAV design
quadrotor endurance |
0.1 | 1 | 2016 | Minimum-energy path generation for a quadrotor UAV · ICRA 2016 |
Methods — techniques the papers use, named apart from their topics
optimal control · 0.2brushless DC motor model · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Minimum-energy path generation for a quadrotor UAVabstractA major limitation of existing battery-powered quadrotor UAVs is their reduced flight endurance. To address this issue, by leveraging the electrical model of a brushless DC motor, we explicitly determine minimum-energy paths between a predefined initial and final configuration of a quadrotor by solving an optimal control problem with respect to the angular accelerations of the four propellers. As a variation on this problem, if the total energy consumption between two boundary states is fixed, minimum-time and/or minimum-control-effort trajectories are computed for the aerial vehicle. The theory is illustrated for the DJI Phantom 2 quadrotor in three realistic scenarios. Fabio Morbidi, Roel Cano, David Lara Alabazares |
ICRA | 2 |