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Jan Ehrensperger

dblp:226/6189 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—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.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 75% Wearable and physiological sensing · 25%
Artificial intelligence
1 paper
Motion planning and robot control · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › motion capture
arm skeleton tracking
0.312018
Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions · ICRA 2018
Human-robot interaction › human behavior modeling
human motion modeling
0.312018
Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions · ICRA 2018
Human-robot interaction
human-robot collaboration
0.312018
Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions · ICRA 2018
Human-robot interaction › human-robot collaboration
safety in collaboration
0.312018
Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions · ICRA 2018
Robotics › Motion planning and robot control
robot control
0.112018
Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions · ICRA 2018

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

control-oriented dynamic model · 0.7collision projection · 0.7
YearPublicationVenuePosition
2018 Safe and Efficient Human-Robot Collaboration Part I: Estimation of Human Arm Motions
abstract
A significant barrier regarding a successful implementation of fenceless robot cells into manufacturing areas with humans is given by the inefficiency due to safety requirements. Robot motions have to be slowed down so that an unexpected collision with a human does not result in human injuries. This velocity reduction leads to longer cycle times and, hence, fenceless robot cells turn out as uneconomic. In this paper, a new approach for human-robot collaboration in assembly tasks is presented. For a better performance of the robot, methods are investigated on how the robot can exploit a maximum performance while maintaining the safety of collaborating humans. For this purpose, the kinematics and dynamics of a human arm are described by a control-oriented dynamic model to determine its capability and reachability. Successful experiments validate the dynamic model as well as a corresponding projection approach for calculating possible movements of the human arm that may lead to a collision with the robot. Finally, this information is used to calculate an admissible path velocity that minimizes the danger of human injuries.
Roman Weitschat, Jan Ehrensperger, Moritz Maier, Harald Aschemann
ICRA2