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Hsu-Chi Chou

dblp:87/6623 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › multi-agent planning
centralized planning
0.012003
Motion planning for a crowd of robots · ICRA 2003
Robotics › Motion planning and robot control › motion planning
multi-robot motion planning
0.012003
Motion planning for a crowd of robots · ICRA 2003
Robotics › Motion planning and robot control
collision avoidance
0.012003
Motion planning for a crowd of robots · ICRA 2003

Methods — techniques the papers use, named apart from their topics

hierarchical sphere tree · 0.0
YearPublicationVenuePosition
2003 Motion planning for a crowd of robots
abstract
Moving 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
ICRA2