Demo Data from EDBT 2026 / reviewers in the wild / expert
Mathew Jellins
dblp:164/8624
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Systems, 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 |
Multi-agent systems · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › multi-agent control
distributed motion control |
0.2 | 1 | 2015 | Distributed centroid estimation and motion controllers for collective transport by multi-robot systems · ICRA 2015 |
Methods — techniques the papers use, named apart from their topics
distributed control · 0.2centroid estimation · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Distributed centroid estimation and motion controllers for collective transport by multi-robot systemsabstractThis paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object. Golnaz Habibi, Zachary Kingston, William Xie, Mathew Jellins, James McLurkin |
ICRA | 4 |
| 2013 | K-Redundant Trees for Safe and Efficient Multi-robot Recovery in Complex Environments
Golnaz Habibi, Lauren Schmidt, Mathew Jellins, James McLurkin |
ISRR | 3 |