Demo Data from SIGIR 2026 / reviewers in the wild / expert
Nele Traichel
dblp:324/6297
· 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 |
Multi-agent systems · 61% Robot navigation and mapping · 30% Motion planning and robot control · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems › collective behavior › swarm behavior › collective motion
flocking |
0.6 | 1 | 2022 | Driving Swarm: A Swarm Robotics Framework for Intelligent Navigation in a Self-organized World · ICRA 2022 |
Robotics › Robot navigation and mapping
multi-robot navigation |
0.6 | 1 | 2022 | Driving Swarm: A Swarm Robotics Framework for Intelligent Navigation in a Self-organized World · ICRA 2022 |
Knowledge, reasoning and agents › Multi-agent systems
swarm robotics |
0.6 | 1 | 2022 | Driving Swarm: A Swarm Robotics Framework for Intelligent Navigation in a Self-organized World · ICRA 2022 |
Robotics › Motion planning and robot control › robot control
trajectory tracking |
0.2 | 1 | 2022 | Driving Swarm: A Swarm Robotics Framework for Intelligent Navigation in a Self-organized World · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
turtlebot3 · 0.6simulation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Driving Swarm: A Swarm Robotics Framework for Intelligent Navigation in a Self-organized WorldabstractImplementing and conducting reproducible experiments on multi-robot hardware platforms are challenging tasks due to variations in hardware, software, and most importantly the intensive implementation effort. In this paper, we aim to present the Driving Swarm software framework which is developed to facilitate the implementation, deployment, supervision, and analysis of multi-robot experiments. We use this framework with the TurtleBot3 hardware platform and measure its performance for two example scenarios: trajectory tracking and flocking behavior, with 5 and 6 robots. The goal of our experiments is to validate the simulation comparing to hardware implementations and to provide a baseline data for further experiments. While the simulated and real robots show similar behavior, we could observe that the simulated behavior is more robust than the real behavior in both scenarios. This effect is observed in the lower tracking error and better obstacle avoidance in both experiments. While the simulation proved to be a valuable tool during the development of the behaviors, the results confirm the importance of conducting experiments on a real-world test bed. Sebastian Mai, Nele Traichel, Sanaz Mostaghim |
ICRA | 2 |