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Tjark Harrie Post

dblp:300/4728 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—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
Motion planning and robot control · 91% Legged, aerial and field robots · 9%

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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › manipulator control
aerial manipulator control
0.312017
Application of substantial and sustained force to vertical surfaces using a quadrotor · ICRA 2017
Robotics › Motion planning and robot control › robot control › stabilization control
attitude stabilization
0.312017
Application of substantial and sustained force to vertical surfaces using a quadrotor · ICRA 2017
Robotics › Motion planning and robot control › robot control › contact control › contact task control › robot force control
contact force control
0.312017
Application of substantial and sustained force to vertical surfaces using a quadrotor · ICRA 2017
Robotics › Legged, aerial and field robots › aerial robots
quadrotor
0.112017
Application of substantial and sustained force to vertical surfaces using a quadrotor · ICRA 2017

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

PID control · 0.3LQR-optimized state feedback · 0.3
YearPublicationVenuePosition
2017 Application of substantial and sustained force to vertical surfaces using a quadrotor
abstract
In the field of aerial robotics, one of the key challenges is to enable aerial manipulators to exert substantial forces on the environment. Enabling this will allow the technology to perform meaningful tasks airborne, such as cleaning or grinding surfaces. While in contact and applying a large, continuous force, control of the UAV's attitude is a challenge. In this work, we show that a regular (PID-based) attitude controller is incapable of stabilizing aerial manipulators that apply physical contact forces on the environment that are comparable to the UAV's weight. We present a novel control algorithm that uses an LQR-optimized state feedback on the roll and yaw angle while in contact. Experiments on a UAV of 1.5 kg show that the proposed controller is capable of applying a contact force of over 15 N - equal to the UAV's weight - sustained for several minutes.
Han W. Wopereis, Jim Johan Hoekstra, Tjark Harrie Post, Gerrit A. Folkertsma, Stefano Stramigioli, Matteo Fumagalli 0001
ICRA3