Demo Data from SIGIR 2026 / reviewers in the wild / expert
Mikael Axelsson
dblp:57/5564
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › mobile robot perception
mobile robot olfaction |
0.0 | 1 | 2001 | Learning to Locate an Odour Source with a Mobile Robot · ICRA 2001 |
Robotics › Robot navigation and mapping › source localization
odor source localization |
0.0 | 1 | 2001 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Learning to Locate an Odour Source with a Mobile RobotabstractWe 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 |
ICRA | 2 |