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Open AccessArticle

Towards Self-Aware Multirotor Formations

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Chair of Software Engineering, University of Würzburg, Am Hubland, 97074 Würzburg, Germany
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Chair of Aerospace Information Technology, University of Würzburg, Josef-Martin-Weg 52, 97074 Würzburg, Germany
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Faculty of Elektrotechnik, University of Applied Sciences Würzburg-Schweinfurt, Ignaz-Schön-Straße 11, 97421 Schweinfurt, Germany
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Author to whom correspondence should be addressed.
Computers 2020, 9(1), 7; https://doi.org/10.3390/computers9010007
Received: 20 December 2019 / Revised: 27 January 2020 / Accepted: 5 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
In the present day, unmanned aerial vehicles become seemingly more popular every year, but, without regulation of the increasing number of these vehicles, the air space could become chaotic and uncontrollable. In this work, a framework is proposed to combine self-aware computing with multirotor formations to address this problem. The self-awareness is envisioned to improve the dynamic behavior of multirotors. The formation scheme that is implemented is called platooning, which arranges vehicles in a string behind the lead vehicle and is proposed to bring order into chaotic air space. Since multirotors define a general category of unmanned aerial vehicles, the focus of this thesis are quadcopters, platforms with four rotors. A modification for the LRA-M self-awareness loop is proposed and named Platooning Awareness. The implemented framework is able to offer two flight modes that enable waypoint following and the self-awareness module to find a path through scenarios, where obstacles are present on the way, onto a goal position. The evaluation of this work shows that the proposed framework is able to use self-awareness to learn about its environment, avoid obstacles, and can successfully move a platoon of drones through multiple scenarios.
Keywords: self-aware computing; unmanned aerial vehicles; multirotors; quadcopters; intelligent transportation systems self-aware computing; unmanned aerial vehicles; multirotors; quadcopters; intelligent transportation systems
MDPI and ACS Style

Kaiser, D.; Lesch, V.; Rothe, J.; Strohmeier, M.; Spieß, F.; Krupitzer, C.; Montenegro, S.; Kounev, S. Towards Self-Aware Multirotor Formations. Computers 2020, 9, 7.

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