Demo Data from EDBT 2026 / reviewers in the wild / expert
Fabrizio Mieli
dblp:41/3554
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2006
—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 |
Robot navigation and mapping · 67% Motion planning and robot control · 33% |
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 › path planning › coverage path planning
multi-robot coverage |
0.1 | 1 | 2006 | Towards Robust on-line Multi-robot Coverage · ICRA 2006 |
Robotics › Robot navigation and mapping › coverage control
online coverage |
0.1 | 1 | 2006 | Towards Robust on-line Multi-robot Coverage · ICRA 2006 |
Robotics › Robot navigation and mapping › coverage control
robust coverage |
0.1 | 1 | 2006 | Towards Robust on-line Multi-robot Coverage · ICRA 2006 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2006 | Towards Robust on-line Multi-robot CoverageabstractArea coverage is an important task for mobile robots, with many real-world applications. In many cases, the coverage has to be completed without the use of a map or any a priori knowledge about the area, a process referred-to as on-line coverage. Previous investigations of multi-robot on-line coverage focused on the improved efficiency gained from the use of multiple robots, but did not formally addressed the potential for greater robustness. We present a novel multi-robot on-line coverage algorithm, based on approximate cell decomposition. We analytically show that the algorithm is complete and robust, in that as long as a single robot is able to move, the coverage would be completed. We analyze the assumptions underlying the algorithm requirements and present a number of techniques for executing it in real robots. We show empirical coverage-time results of running the algorithm in two different environments and several group sizes Noam Hazon, Fabrizio Mieli, Gal A. Kaminka |
ICRA | 2 |