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Yu-Rwei Gwo

dblp:139/7196 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1991
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning
0.011991
Dynamic motion planning of autonomous vehicles · IEEE Trans. Robotics Autom. 1991
Robotics › Motion planning and robot control › motion planning › mobile robot motion planning
terrain-aware planning
0.011991
Dynamic motion planning of autonomous vehicles · IEEE Trans. Robotics Autom. 1991
Robotics › Motion planning and robot control › motion planning › optimal motion planning
time-optimal motion planning
0.011991
Dynamic motion planning of autonomous vehicles · IEEE Trans. Robotics Autom. 1991
Robotics › Motion planning and robot control › motion planning
vehicle motion planning
0.011991
Dynamic motion planning of autonomous vehicles · IEEE Trans. Robotics Autom. 1991

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

local optimization · 0.0b-spline · 0.0
YearPublicationVenuePosition
1991 Dynamic motion planning of autonomous vehicles
abstract
A method for planning the motions of autonomous vehicles moving on general terrains is presented that obtains the geometric path and vehicle speeds that minimize motion time considering vehicle dynamics, terrain topography, obstacles, and surface mobility. The terrain is represented by a smooth cubic B patch, and the geometric path consists of a B spline curve mapped to the surface. The time-optimal motions are computed by first obtaining the best obstacle-free path from all paths represented by a uniform grid. This path is further optimized using a local optimization procedure, using the optimal motion time along the path as the cost function and the control points of a B spline as the optimizing parameters. Examples are presented that demonstrate the method for a simple dynamic model of a vehicle moving on mountainous terrain.>
Zvi Shiller, Yu-Rwei Gwo
IEEE Trans. Robotics Autom.2