Katherine E. Riojas

dblp:221/4272 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-1845-6634ORCID · reported

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
Robot manipulation · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › continuum robot
continuum robot modeling
0.412020
A Dynamic Model for Concentric Tube Robots · IEEE Trans. Robotics 2020
Robotics › Robot manipulation › continuum robot
cosserat rod model
0.412020
A Dynamic Model for Concentric Tube Robots · IEEE Trans. Robotics 2020

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

partial differential equations · 0.4implicit finite differences · 0.4
YearPublicationVenuePosition
2020 A Dynamic Model for Concentric Tube Robots
abstract
Existing static and kinematic models of concentric tube robots are based on the ordinary differential equations of a static Cosserat rod. In this paper, we provide the first dynamic model for concentric tube continuum robots by adapting the partial differential equations of a dynamic Cosserat rod to describe the coupled inertial dynamics of precurved concentric tubes. This generates an initial-boundary-value problem that can capture robot vibrations over time. We solve this model numerically at high time resolutions using implicit finite differences in time and arc length. This approach is capable of resolving the high-frequency torsional dynamics that occur during unstable "snapping" motions and provides a simulation tool that can track the true robot configuration through such transitions. Further, it can track slower oscillations associated with bending and torsion as a robot interacts with tissue at real-time speeds. Experimental verification of the model shows that this wide range of effects is captured efficiently and accurately.
John Till, Vincent A. Aloi, Katherine E. Riojas, Patrick L. Anderson, Robert J. Webster III, D. Caleb Rucker
IEEE Trans. Robotics3