Demo Data from EDBT 2026 / reviewers in the wild / expert
Max Queenan
dblp:151/9766
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—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 |
3D vision · 38% Legged, aerial and field robots · 38% Motion planning and robot control · 12% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › bio-inspired robot
snake robot |
0.2 | 1 | 2014 | Visual sensing for developing autonomous behavior in snake robots · ICRA 2014 |
Computer vision › 3D vision › range sensing › 3d scanning
structured light scanning |
0.2 | 1 | 2014 | Visual sensing for developing autonomous behavior in snake robots · ICRA 2014 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning › abstraction in planning
high-level planning |
0.1 | 1 | 2014 | Visual sensing for developing autonomous behavior in snake robots · ICRA 2014 |
Robotics › Motion planning and robot control
motion planning |
0.1 | 1 | 2014 | Visual sensing for developing autonomous behavior in snake robots · ICRA 2014 |
Methods — techniques the papers use, named apart from their topics
structured light · 0.23d mapping · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Visual sensing for developing autonomous behavior in snake robotsabstractSnake robots are uniquely qualified to investigate a large variety of settings including archaeological sites, natural disaster zones, and nuclear power plants. For these applications, modular snake robots have been tele-operated to perform specific tasks using images returned to it from an onboard camera in the robots head. In order to give the operator an even richer view of the environment and to enable the robot to perform autonomous tasks we developed a structured light sensor that can make three-dimensional maps of the environment. This paper presents a sensor that is uniquely qualified to meet the severe constraints in size, power and computational footprint of snake robots. Using range data, in the form of 3D pointclouds, we show that it is possible to pair high-level planning with mid-level control to accomplish complex tasks without operator intervention. Hugo Ponte, Max Queenan, Chaohui Gong, Christoph Mertz, Matthew J. Travers, Florian Enner, Martial Hebert, Howie Choset |
ICRA | 2 |