Demo Data from EDBT 2026 / reviewers in the wild / expert
Jean Dussine
dblp:324/6217
· 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 · 50% Motion planning and robot control · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › aerial robots › aerial physical interaction
aerial manipulation |
0.6 | 1 | 2022 | Optimal Design and Control of an Aerial Manipulator with Elastic Suspension Using Unidirectional Thrusters · ICRA 2022 |
Robotics › Motion planning and robot control › design optimization
robot design optimization |
0.6 | 1 | 2022 | Optimal Design and Control of an Aerial Manipulator with Elastic Suspension Using Unidirectional Thrusters · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
workspace optimization · 0.6trajectory tracking control · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Optimal Design and Control of an Aerial Manipulator with Elastic Suspension Using Unidirectional ThrustersabstractAerial Manipulators with Elastic Suspension (AMES) may be seen as a hybrid robot mixing properties of classical Aerial Manipulators (AMs) and Cable-Driven Parallel Robots (CDPRs). The optimal design and control of an AMES using unidirectional thrusters are considered in this paper. To maximize the workspace, an optimization algorithm is proposed. The position and orientation of the thrusters are optimized by adapting methods borrowed from both the AM and CDPR communities. The resulting design is used to build a prototype. Preliminary experimentations are carried out to validate the theoretical workspace and assess the trajectory tracking performance of this AMES. Experiments highlight the significant improvements with respect to a previous suboptimal prototype. Miguel Arpa Perozo, Jean Dussine, Arda Yigit, Loïc Cuvillon, Sylvain Durand, Jacques Gangloff |
ICRA | 2 |