Demo Data from EDBT 2026 / reviewers in the wild / expert
Marco Tranzatto
dblp:207/8554
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
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 |
Reinforcement learning · 87% Legged, aerial and field robots · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › exploration › autonomous exploration › mobile robot exploration
exploration path planning |
0.6 | 1 | 2022 | Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots · ICRA 2022 |
Machine learning › Reinforcement learning › exploration
multi-robot exploration |
0.6 | 1 | 2022 | Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots · ICRA 2022 |
Methods — techniques the papers use, named apart from their topics
multi-robot coordination · 0.6frontier-based exploration · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial RobotsabstractThis paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for 45 min, as well as a longer simulation study with three systems. Mihir Kulkarni, Mihir Dharmadhikari, Marco Tranzatto, Samuel Zimmermann, Victor Reijgwart, Paolo De Petris, Huan Nguyen 0003, Nikhil Khedekar, Christos Papachristos, Lionel Ott, Roland Siegwart, Marco Hutter 0001, Kostas Alexis |
ICRA | 3 |