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Mikael Axelsson

dblp:57/5564 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2001
—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
Robot navigation and mapping · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › mobile robot perception
mobile robot olfaction
0.012001
Learning to Locate an Odour Source with a Mobile Robot · ICRA 2001
Robotics › Robot navigation and mapping › source localization
odor source localization
0.012001
Learning to Locate an Odour Source with a Mobile Robot · ICRA 2001

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

recurrent neural network · 0.0
YearPublicationVenuePosition
2001 Learning to Locate an Odour Source with a Mobile Robot
abstract
We address the problem of enabling a mobile robot to locate a stationary odour source using an electronic nose constructed from gas sensors. On the hardware side, we use a stereo nose architecture consisting of two parallel chambers, each containing an identical set of sensors. On the software side, we use a recurrent artificial neural network to learn the direction to a stationary source from a time series of sensor readings. This contrasts with previous approaches, that rely on the existence of a model of the sensor's dynamics. The complete system is able to orient and turn towards the source. An experimental validation was carried out to evaluate the performance of the system.
Tom Duckett, Mikael Axelsson, Alessandro Saffiotti
ICRA2