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Eleonora Mariotti

dblp:246/7746 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 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 · 67% Haptics and multimodal interaction · 33%
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
Human-robot interaction › physical human-robot interaction
admittance control
0.412019
Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor · ICRA 2019
Haptics and multimodal interaction › haptic rendering
collision detection
0.412019
Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor · ICRA 2019
Human-robot interaction
physical human-robot interaction
0.412019
Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor · ICRA 2019
Robotics › Motion planning and robot control
robot control
0.112019
Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor · ICRA 2019
Robotics › Motion planning and robot control › robot control
torque control
0.112019
Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor · ICRA 2019

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

kinematic estimation · 0.8admittance control law · 0.8
YearPublicationVenuePosition
2019 Admittance Control for Human-Robot Interaction Using an Industrial Robot Equipped with a F/T Sensor
abstract
We present an approach to safe physical Human-Robot Interaction (pHRI) for industrial robots, including collision detection, distinguishing accidental from intentional contacts, and achieving collaborative tasks. Typical industrial robots have a closed control architecture that accepts only velocity/position reference inputs, there are no joint torque sensors, and little or no information is available to the user on robot dynamics and on low-level joint controllers. Nonetheless, taking also advantage of the presence of a Force/Torque (F/T) sensor at the end-effector, a safe pHRI strategy based on kinematic information, on measurements from joint encoders and motor currents, and on end-effector forces/torques can be realized. An admittance control law has been implemented for collaboration in manual guidance mode, with whole-body collision detection in place both when the robot is in autonomous operation and when is simultaneously collaborating with a human. Several pHRI experiments validate the approach on a KUKA KR5 Sixx R650 robot equipped with an ATI F/T sensor.
Eleonora Mariotti, Emanuele Magrini, Alessandro De Luca 0001
ICRA1