Demo Data from VLDB 2026 / reviewers in the wild / expert
Hsu-Chi Chou
dblp:87/6623
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—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 · 56% Planning, search and constraint satisfaction · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › multi-agent planning
centralized planning |
0.0 | 1 | 2003 | Motion planning for a crowd of robots · ICRA 2003 |
Robotics › Motion planning and robot control › motion planning
multi-robot motion planning |
0.0 | 1 | 2003 | Motion planning for a crowd of robots · ICRA 2003 |
Robotics › Motion planning and robot control
collision avoidance |
0.0 | 1 | 2003 | Motion planning for a crowd of robots · ICRA 2003 |
Methods — techniques the papers use, named apart from their topics
hierarchical sphere tree · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Motion planning for a crowd of robotsabstractMoving a crowd of robots or avatars from their current configurations to some destination area without causing collisions is a challenging motion-planning problem because the high degrees of freedom involved. Two approaches are often used for this type of problems: decoupled and centralized. The tradeoff of these two approaches is that the decoupled approach is considered faster while the centralized approach has the advantage of being complete. In this paper, we propose an efficient centralized planner that is much faster than the traditional randomized planning approaches. This planner uses a hierarchical sphere tree structure to group robots dynamically. By taking advantage of the problem characteristics on independently moving robots, we are able to design a practical planner with the centralized approach when the number of robots is rather large. We use several simulation examples to demonstrate the efficiency and effectiveness of the planner. Tsai-Yen Li, Hsu-Chi Chou |
ICRA | 2 |