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Nicholas A. Strohmeyer

dblp:383/4456 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

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 · 70% Motion planning and robot control · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › teleoperation
shared control
0.812024
A Semi-Autonomous Data-Driven Shared Control Framework for Robotic Manipulation and Cutting of an Unknown Deformable Tissue · ICRA 2024
Robotics › Robot manipulation › medical robotics
surgical robotics
0.812024
A Semi-Autonomous Data-Driven Shared Control Framework for Robotic Manipulation and Cutting of an Unknown Deformable Tissue · ICRA 2024
Robotics › Robot manipulation
telemanipulation
0.812024
A Semi-Autonomous Data-Driven Shared Control Framework for Robotic Manipulation and Cutting of an Unknown Deformable Tissue · ICRA 2024
Robotics › Robot manipulation
deformable object manipulation
0.212024
A Semi-Autonomous Data-Driven Shared Control Framework for Robotic Manipulation and Cutting of an Unknown Deformable Tissue · ICRA 2024

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

reduced-order trajectory planning · 0.8data-driven learning · 0.8adaptive control · 0.8
YearPublicationVenuePosition
2024 A Semi-Autonomous Data-Driven Shared Control Framework for Robotic Manipulation and Cutting of an Unknown Deformable Tissue
abstract
In this work, we propose a semi-autonomous scheme to synergistically share the complicated task of manipulation and cutting of an unknown deformable tissue (U-DT) between a remote surgeon and a surgical robot. Particularly, utilizing the da Vinci Research Kit (dVRK) platform, we have designed and successfully demonstrated a fully functional shared control scheme for an autonomous tensioning and tele-cutting of a U-DT. We have shown the system’s ability to cooperate with a remote surgeon by leveraging an online data-driven learning and adaptive control method coupled with a reduced-order trajectory planning module that depends on just two parameters. By performing 25 experiments on custom-designed silicon phantoms and defining a set of success/failure metrics, we have put forward findings that establish a causal relationship between these two important parameters and the success or failure of the performed experiments.
Nicholas A. Strohmeyer, Ji Hwan Park, Braden P. Murphy, Farshid Alambeigi
ICRA1