Demo Data from VLDB 2026 / reviewers in the wild / expert
Eva Noll
dblp:94/6934
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1996
—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 |
Motion planning and robot control · 100% |
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
path planning |
0.0 | 1 | 1996 | An explicit path planner to facilitate reactive control and terrain preferences · ICRA 1996 |
Robotics › Motion planning and robot control › robot control
reactive control |
0.0 | 1 | 1996 | An explicit path planner to facilitate reactive control and terrain preferences · ICRA 1996 |
Robotics › Motion planning and robot control › path planning › mobile robot path planning
terrain-aware path planning |
0.0 | 1 | 1996 | An explicit path planner to facilitate reactive control and terrain preferences · ICRA 1996 |
Methods — techniques the papers use, named apart from their topics
wavefront propagation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | An explicit path planner to facilitate reactive control and terrain preferencesabstractThis paper presents a new approach to reactive control utilizing the output of an explicit path planner which considers terrain preferences. To demonstrate the concept, a wavefront propagation, type of planner algorithm, Trulla, is integrated into a reactive framework and used as part of the overall architecture. Terrain preferences are represented as weights or costs which are considered in generating a near optimal path. The algorithm is demonstrated on the CSM Denning-Branch MRV4 mobile robot for three scenarios: navigation out of a box canyon, planning with terrain preferences, and opportunistic path improvement. Robin R. Murphy, Ken Hughes, Eva Noll |
ICRA | 3 |