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Giovanni D'Urso

dblp:214/3725 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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 · 38% Planning, search and constraint satisfaction · 38% Legged, aerial and field robots · 23%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › game tree search
monte carlo tree search
0.512021
Hierarchical MCTS for Scalable Multi-Vessel Multi-Float Systems · ICRA 2021
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.512021
Hierarchical MCTS for Scalable Multi-Vessel Multi-Float Systems · ICRA 2021
Robotics › Legged, aerial and field robots › field robotics
environmental monitoring
0.112021
Hierarchical MCTS for Scalable Multi-Vessel Multi-Float Systems · ICRA 2021
Robotics › Legged, aerial and field robots
underwater robotics
0.112021
Hierarchical MCTS for Scalable Multi-Vessel Multi-Float Systems · ICRA 2021

Methods — techniques the papers use, named apart from their topics

simulation · 0.5Dec-MCTS · 0.5
YearPublicationVenuePosition
2021 Hierarchical MCTS for Scalable Multi-Vessel Multi-Float Systems
abstract
Systems of multiple low-cost, underactuated floats combined with fully actuated surface vessels can improve the scalability and cost-effectiveness of autonomous systems for marine science and environmental monitoring. Here, we consider a coordination problem where surface vessels must drop off floats at locations such that they are likely to drift to observe given points of interest, and later must pick up the floats for redeployment. We define the Multi-Vessel Multi-Float (MVMF) problem and present a hierarchical solution based on the Dec-MCTS algorithm. Our solution defines customised sampling, rollout, and action generation algorithms to accommodate the problem’s large search space and provide computational performance sufficient for practical application. We report analytical and simulation results that demonstrate the computational efficiency of our method and validate its behaviour in practical problems. These results immediately enable field experiments to progress the development of this exciting concept in multi-robot marine systems.
Giovanni D'Urso, James Ju Heon Lee, Oscar Pizarro, Chanyeol Yoo, Robert Fitch
ICRA1