Demo Data from EDBT 2026 / reviewers in the wild / expert
Thomas Laurent Peeters
dblp:353/5877
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—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 |
Robot navigation and mapping · 64% Multi-agent systems · 28% Reinforcement learning · 8% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
environment mapping |
0.7 | 1 | 2023 | Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge · ICRA 2023 |
Robotics › Robot navigation and mapping › robot mapping › environment modeling
gas distribution mapping |
0.7 | 1 | 2023 | Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge · ICRA 2023 |
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination |
0.7 | 1 | 2023 | Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge · ICRA 2023 |
Machine learning › Reinforcement learning
exploration |
0.2 | 1 | 2023 | Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge · ICRA 2023 |
Robotics › Robot navigation and mapping › mobile robot navigation › navigation planning
informative path planning |
0.2 | 1 | 2023 | Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge · ICRA 2023 |
Methods — techniques the papers use, named apart from their topics
information-gain navigation · 0.7high-fidelity simulation · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental KnowledgeabstractEnvironmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled with multi-robot systems improve the outcome of an environmental mapping mission by focusing more efficiently on informative areas. This direction is yet to be explored in the context of gas mapping, which presents peculiar challenges due to the hard-to-sense and expensive-to-model nature of the underlying phenomenon. In this paper, we introduce the application of a multi-robot system to a gas mission with severe time constraints. We study the impact of information-based navigation strategies, coupled with increasing levels of coordination among the robots, on information gathering and consequent map reconstruction performance. We also focus on proposing solutions that inject additional knowledge into the system to enhance the final mapping outcome. We tested the strategies through extensive high-fidelity simulation experiments, and we compared the proposed approaches to three relevant baseline methods. Chiara Ercolani, Shashank Mahendra Deshmukh, Thomas Laurent Peeters, Alcherio Martinoli |
ICRA | 3 |