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Giulio Troso

dblp:41/3660 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

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% Robot navigation and mapping · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Reinforcement learning
exploration
0.012004
The SRT Method: Randomized Strategies for Exploration · ICRA 2004
Machine learning › Reinforcement learning › exploration › autonomous exploration
sensor-based exploration
0.012004
The SRT Method: Randomized Strategies for Exploration · ICRA 2004
Robotics › Robot navigation and mapping
map building
0.012004
The SRT Method: Randomized Strategies for Exploration · ICRA 2004

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

randomized incremental tree · 0.0perception strategies · 0.0
YearPublicationVenuePosition
2004 The SRT Method: Randomized Strategies for Exploration
abstract
We present a method for sensor-based exploration of unknown environments by a mobile robot. The method is based on the randomized incremental generation of a data structure called sensor-based random tree (SRT), which represents a roadmap of the explored area with an associated safe region. Different exploration strategies may be obtained by instantiating the general method with different perception techniques. Two such techniques are discussed: the first, conservative and particularly suited to noisy sensors, results in an exploration strategy called SRT-Ball. The second perception technique is more confident, and the corresponding strategy is called SRT-Star. The two strategies are critically compared by simulations as well as by experiments on the MagellanPro robot.
Giuseppe Oriolo, Marilena Vendittelli, Luigi Freda, Giulio Troso
ICRA4