Abstract

One of the most interesting challenges of the next few years will be airspace system automation. This process will involve different aspects such as air traffic management, aircraft and airport operations and guidance and navigation systems. The use of unmanned aerial system for civil missions will be one of the most important steps in this automation process. In this article, an air traffic management oriented conflict detection & resolution algorithm that models air traffic management operative technique as avoidance manoeuvres in order to self-separate the unmanned aerial vehicle from piloted air traffic is presented. As a first step, a geometric analysis identifies all possible unmanned aerial vehicle routes among the mission targets and related potential conflicts with piloted air traffic. For each potential conflict, air traffic management operative techniques are used to model different options of conflict resolution: vertical and horizontal avoidance, speed regulation, holding patterns and rerouting. The performances of a reference unmanned aerial vehicle are used to estimate the cost of each possible sub-route and, in case of conflict, the cost of each possible avoidance manoeuvre. In this way, the unmanned aerial vehicle mission is modelled as a combinatorial optimization problem that concerns the sequencing of both targets and conflict resolution options. As output, a conflict free route that minimizes the air traffic impact over the mission is provided. Simulation results over real air traffic data show how this approach could be useful for future common management of piloted and non-piloted air traffic.


Original document

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

http://journals.sagepub.com/doi/full-xml/10.1177/0954410012439975,
http://dx.doi.org/10.1177/0954410012439975 under the license http://journals.sagepub.com/page/policies/text-and-data-mining-license
https://trid.trb.org/view/1250233,
https://cris.unibo.it/handle/11585/114902,
http://journals.sagepub.com/doi/10.1177/0954410012439975,
http://pig.sagepub.com/content/227/4/687.abstract,
https://academic.microsoft.com/#/detail/2163818789
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Published on 01/01/2012

Volume 2012, 2012
DOI: 10.1177/0954410012439975
Licence: Other

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