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Joshua Bross

dblp:09/8369 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2010
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › motion planning › manipulation planning
deformable object motion planning
0.112010
Deformable robot motion planning in a reduced-dimension configuration space · ICRA 2010
Robotics › Motion planning and robot control › motion planning
sampling-based motion planning
0.112010
Deformable robot motion planning in a reduced-dimension configuration space · ICRA 2010

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

principal component analysis · 0.1energy constraints · 0.1
YearPublicationVenuePosition
2010 Deformable robot motion planning in a reduced-dimension configuration space
abstract
Motion planning of deformable objects is challenging due to the high degrees-of-freedom inherent in deformation as well as the computational cost of producing physically accurate deformations. This paper develops a method for fast, physically plausible deformations using a sampling-based planner in a reduced dimensionality configuration space containing position, orientation, and a reduced deformation space produced using principal component analysis. A key element of the presented approach is the use of energy constraints in the configuration space designed to remove implausible deformations that may be produced by the principal component analysis dimension reduction process. This approach is tested on several deforming robot planning tasks, such as a sphere fitting through slots, a bendable bar in a maze, and a deforming plate in tunnels, with collision free paths using plausible deformations found in a few minutes.
Arthur Mahoney, Joshua Bross
ICRA2