Automated Aerial Screwing with a Fully Actuated Aerial Manipulator

Conference Paper (2022)
Author(s)

Micha Schuster (Technische Universität Dresden)

David Bernstein (Technische Universität Dresden)

Paul Reck (Technische Universität Dresden)

Salua Hamaza (TU Delft - Control & Simulation)

Michael Beitelschmidt (Technische Universität Dresden)

Research Group
Control & Simulation
Copyright
© 2022 Micha Schuster, David Bernstein, Paul Reck, S. Hamaza, Michael Beitelschmidt
DOI related publication
https://doi.org/10.1109/IROS47612.2022.9981979
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Micha Schuster, David Bernstein, Paul Reck, S. Hamaza, Michael Beitelschmidt
Research Group
Control & Simulation
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
3340-3347
ISBN (electronic)
9781665479271
Reuse Rights

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Abstract

The tasks that unmanned aerial vehicles (UAVs) have taken upon have progressively grown in complexity over the years, alongside with the level of autonomy with which they are carried out. In this work, we present an example
of aerial screwing operations with a fully-actuated tilt-rotor platform. Key contributions include a new control framework to automate screwing operations through a robust hole search and in-hole detection algorithm. These are achieved without a-priori knowledge of the exact hole location, and without
the use of external tools, such as vision based hole detection or force sensors. Wrench coupling is implemented to account for the platform's kinematic constraints during screwing. The application of a constant contact force and a compliant response
to induced disturbances are obtained with the use of admittance
control. The full framework is validated with extensive flight
experiments that demonstrate the effectiveness of each subsystem,
as well as the complete architecture. We also validate
the robustness of the detection algorithm against false positives.
Within the results we demonstrate the ability to perform the
automated task with a 86% success rate over 35 flights, and
measured hole search time of 9s (median value).

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