Demo Data from EDBT 2026 / reviewers in the wild / expert
Korhan Turker
dblp:116/6469
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2012
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Legged, aerial and field robots · 67% Motion planning and robot control · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control
locomotion control |
0.1 | 1 | 2012 | Step negotiation with wheel traction: a strategy for a wheel-legged robot · ICRA 2012 |
Robotics › Legged, aerial and field robots › legged robots
step climbing |
0.1 | 1 | 2012 | Step negotiation with wheel traction: a strategy for a wheel-legged robot · ICRA 2012 |
Robotics › Legged, aerial and field robots
wheel-legged robot |
0.1 | 1 | 2012 | Step negotiation with wheel traction: a strategy for a wheel-legged robot · ICRA 2012 |
Methods — techniques the papers use, named apart from their topics
wheel traction control · 0.1state machine · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Step negotiation with wheel traction: a strategy for a wheel-legged robotabstractThis paper presents a quasi-static step climbing behaviour for a minimal sensing wheel-legged quadruped robot called PAW. In the quasi-static climbing maneuver, the robot benefits from wheel traction and uses its legs to reconfigure itself with respect to the step during the climb. The control methodology with the corresponding controller parameters is determined and the state machine for the maneuver is developed. With this controller, PAW is able to climb steps higher than its body clearance. Furthermore, any step height up to this maximum achievable height can be negotiated autonomously with a single set of controller parameters, without knowledge of the step height or distance to the step. Korhan Turker, Inna Sharf, Michael Trentini |
ICRA | 1 |