Demo Data from EDBT 2026 / reviewers in the wild / expert
Alex C.-C. Meng
dblp:32/2808
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1988
—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 |
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
collision-free path planning |
0.0 | 1 | 1988 | Dynamic motion replanning for unexpected obstacles · ICRA 1988 |
Robotics › Motion planning and robot control › motion planning › replanning
dynamic replanning |
0.0 | 1 | 1988 | Dynamic motion replanning for unexpected obstacles · ICRA 1988 |
Robotics › Motion planning and robot control
motion planning |
0.0 | 1 | 1988 | Dynamic motion replanning for unexpected obstacles · ICRA 1988 |
Methods — techniques the papers use, named apart from their topics
visibility graph · 0.0retraction method · 0.0local world model patching · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1988 | Dynamic motion replanning for unexpected obstaclesabstractMotion-planning problems are discussed for a mobile robot with an incomplete map. For the given start and goal positions, the robot will preplan a collision-free path based on the given map, this is known as the find-path problem. A solution of the find-path problem is presented for randomly shaped obstacles using an improved retraction method, namely, retraction with local replacement by visibility graph. Three different versions of a motion replanning algorithm are presented that patch the world model and regenerate a path from the current location to the goal location. In this scheme, the recomputation of a completely updated world model is avoided by patching the locally updated environment.> Alex C.-C. Meng |
ICRA | 1 |