Demo Data from EDBT 2026 / reviewers in the wild / expert
Sanket A. Salunkhe
dblp:399/1609
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—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 · 72% Legged, aerial and field robots · 28% |
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
fixed-wing UAV |
0.9 | 1 | 2025 | Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial Systems · ICRA 2025 |
Robotics › Motion planning and robot control
trajectory planning |
0.9 | 1 | 2025 | Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial Systems · ICRA 2025 |
Robotics › Motion planning and robot control
trajectory planning and tracking |
0.9 | 1 | 2025 | Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial Systems · ICRA 2025 |
Robotics › Motion planning and robot control › trajectory planning
differential flatness |
0.3 | 1 | 2025 | Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial Systems · ICRA 2025 |
Robotics › Motion planning and robot control
robot control |
0.3 | 1 | 2025 | Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial Systems · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.9real-world experiments · 0.9differential flatness · 0.9coordinated flight · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Trajectory Planning and Control for Differentially Flat Fixed-Wing Aerial SystemsabstractEfficient real-time trajectory planning and control for fixed-wing unmanned aerial vehicles is challenging due to their non-holonomic nature, complex dynamics, and the additional uncertainties introduced by unknown aerodynamic effects. In this paper, we present a fast and efficient real-time trajectory planning and control approach for fixed-wing unmanned aerial vehicles, leveraging the differential flatness property of fixed-wing aircraft in coordinated flight conditions to generate dynamically feasible trajectories. The approach provides the ability to continuously replan trajectories, which we show is useful to dynamically account for the curvature constraint as the aircraft advances along its path. Extensive simulations and real-world experiments validate our approach, showcasing its effectiveness in generating trajectories even in challenging conditions for small FW such as wind disturbances. Luca Morando, Sanket A. Salunkhe, Nishanth Bobbili, Jeffrey Mao, Luca Masci, Cristino de Souza, Hung Nguyen 0009, Giuseppe Loianno |
ICRA | 2 |