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Klaus W. G. Eichhorn

dblp:08/8370 · 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
Robot manipulation · 50% Motion planning and robot control · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
grasping
0.112010
Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery · ICRA 2010
Robotics › Motion planning and robot control › robot control
hybrid control
0.112010
Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery · ICRA 2010
Robotics › Robot manipulation
manipulation primitives
0.112010
Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery · ICRA 2010
Robotics › Motion planning and robot control › robot control › hybrid control
mode switching control
0.112010
Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery · ICRA 2010
Medical and health informatics › surgical robotics
robot-assisted surgery
0.012010
Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery · ICRA 2010
Medical and health informatics
surgical robotics
0.012010
Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery · ICRA 2010

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

hybrid controller · 0.2biomechanical model · 0.2
YearPublicationVenuePosition
2010 Demonstration of a prototype for robot assisted Endoscopic Sinus Surgery
abstract
In this video we show our current prototype for robot assisted endoscopy. The system requires only few and simple instructions from the surgeon, in order to guide the endoscope in an intelligent, autonomous, and safe way: The surgeon tells what to do and the robot decides how to carry out the task by choosing the best manipulation primitive in every control cycle. Several sensors are integrated into the decision process: The endoscope camera, a stereo camera system, a force/torque sensor, a biomechanical model based on CT data and statistical knowledge, a position and velocity sensor for the manipulator, and interface devices like a foot switch. The executed manipulation primitive is handled by a hybrid controller allowing to switch the control mode (e.g. trajectory following or force control) for each degree of freedom of the task frame individually.
Markus Rilk, Daniel Kubus, Friedrich M. Wahl, Klaus W. G. Eichhorn, Ingo Wagner, Friedrich Bootz
ICRA4