Abstract

Safety in the operations of UAVs (Unmanned Aerial Vehicles) depends on the current and future reduction of technical barriers and on the improvements related to their autonomous capabilities. Since the early stages, aviation has been based on pilots and Air Traffic Controllers that take decisions to make aircraft follow their routes while avoiding collisions. RPA (Remotely Piloted Aircraft) can still involve pilots as they are UAVs controlled from ground, but need the definition of common rules, of a dedicated Traffic Controller and exit strategies in the case of lack of communication between the Ground Control Station and the aircraft. On the other hand, completely autonomous aircraft are currently banned from civil airspace, but researchers and engineers are spending great effort in developing methodologies and technologies to increase the reliability of fully autonomous flight in view of a safe and efficient integration of UAVs in the civil airspace. This paper deals with the design of a collision avoidance system based on a Distributed Model Predictive Controller (DMPC) for trajectory tracking, where anticollision constraints are defined in accordance with the Right of Way rules, as prescribed by the International Civil Aviation Organization (ICAO) for human piloted flights. To reduce the computational burden, the DMPC is formulated as a Mixed Integer Quadratic Programming optimization problem. Simulation results are shown to prove the effectiveness of the approach, also in the presence of a densely populated airspace.


Original document

The different versions of the original document can be found in:

http://link.springer.com/article/10.1007/s10846-019-01047-5/fulltext.html,
http://dx.doi.org/10.1007/s10846-019-01047-5 under the license http://www.springer.com/tdm
https://link.springer.com/article/10.1007/s10846-019-01047-5,
https://dialnet.unirioja.es/servlet/articulo?codigo=7271523,
https://doi.org/10.1007/s10846-019-01047-5,
https://academic.microsoft.com/#/detail/2953195108
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Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1007/s10846-019-01047-5
Licence: Other

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