EDBT 2026 Demo / reviewers in the wild / expert
Jelle Juhl
dblp:246/7774
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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 · 81% Multi-agent systems · 19% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › multi-robot control
multi-robot formation control |
0.4 | 1 | 2019 | Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments · ICRA 2019 |
Robotics › Motion planning and robot control
collision avoidance |
0.1 | 1 | 2019 | Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments · ICRA 2019 |
Knowledge, reasoning and agents › Multi-agent systems › consensus
distributed consensus |
0.1 | 1 | 2019 | Distributed Multi-Robot Formation Splitting and Merging in Dynamic Environments · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
distributed consensus · 0.4convex region intersection graph · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Distributed Multi-Robot Formation Splitting and Merging in Dynamic EnvironmentsabstractThis paper presents a distributed method for splitting and merging of multi-robot formations in dynamic environments with static and moving obstacles. Splitting and merging actions rely on distributed consensus and can be performed to avoid obstacles. Our method accounts for the limited communication range and visibility radius of the robots and relies on the communication of obstacle-free convex regions and the computation of an intersection graph. In addition, our method is able to detect and recover from (permanent and temporary) communication and motion faults. Finally, we demonstrate the applicability and scalability of the proposed method in simulations with up to sixteen quadrotors and real-world experiments with a team of four quadrotors. Hai Zhu 0002, Jelle Juhl, Laura Ferranti, Javier Alonso-Mora |
ICRA | 2 |