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Marco Tranzatto

dblp:207/8554 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning › exploration › autonomous exploration › mobile robot exploration
exploration path planning
0.612022
Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots · ICRA 2022
Machine learning › Reinforcement learning › exploration
multi-robot exploration
0.612022
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
YearPublicationVenuePosition
2022 Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots
abstract
This 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
ICRA3