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Simon Notheis

dblp:05/9808 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2014
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 2 · 2 first-author

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.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 50% Interaction techniques and input · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › teleoperation
telemanipulation
0.212014
Evaluation of a method for intuitive telemanipulation based on view-dependent mapping and inhibition of movements · ICRA 2014

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

user study · 0.2
YearPublicationVenuePosition
2014 Evaluation of a method for intuitive telemanipulation based on view-dependent mapping and inhibition of movements
abstract
In this paper we present a novel approach for intuitive telemanipulation in Cartesian space and discuss the results of a user study evaluating different aspects of our approach. The proposed method inhibits certain degrees of freedom based on the current viewpoint. Together with automatic mapping of the input device to corresponding motion axes, our approach provides a very intuitive method for controlling the telemanipulation system while reducing the mental workload of the operator and therefore the amount of erroneous commands. Similar principles apply for controlling the viewpoint of real or virtual cameras to facilitate manipulation or navigation tasks.
Simon Notheis, Björn Hein, Heinz Wörn
ICRA1
2010 Skill-based telemanipulation by means of intelligent robots
abstract
In order to enable robots to execute highly dynamic tasks in dangerous or remote environments, a semiautomatic teleoperation concept has been developed and will be presented in this paper. It relies on a modular software architecture, which allows intuitive control over the robot and compensates latency-based risks by using Augmented Reality techniques together with path prediction and collision avoidance to provide the remote user with visual feedback about the tasks and skills that will be executed. Based on this architecture different skills with high dynamics are integrated in the robot control, so that they can be executed autonomously without the delayed feedback of the user. The skill-based grasping by adherence of smooth or fragile objects during a remote controlled picking and placing task will be exemplary presented.
Simon Notheis, Giulio Milighetti, Björn Hein, Heinz Wörn, Jürgen Beyerer
IROS1