Assessing the Robustness of a UAS Detect & Avoid Algorithm

Cyril Allignol 1 Nicolas Barnier 1 Nicolas Durand 1 Guido Manfredi 1 Éric Blond 2
1 ENAC - Ecole Nationale de l'Aviation Civile
2 DTI - Direction de la technique et de l'innovation de la DGAC
Abstract : In this article, we evaluate the robustness of a detect and avoid algorithm designed for the integration of UASs in terminal control areas. This assessment relies on a realistic modeling of navigation accuracy on positions and velocities and was carried out on thousands of scenarios built from recorded commercial traffic trajectories. The tested scenarios involved two different types of UASs – flying at 80 kts and 160 kts – with various missions, and three strategies for separation: one focussing on the separation distance, one focussing on the UAS mission and and combination of both. Fast-time simulation was used to evaluate each scenario against a wide range of accuracy levels corresponding to required navigation precision standards and linked to on-board navigation and communication systems. Experiments reveal a strong robustness of the separation algorithm up to relatively high uncertainty levels, indicating that UASs equipped with low accuracy navigation systems can still manage proper separation. However, the maneuvering cost for separation increases when the accuracy deteriorates. Nevertheless, a UAS with GPS-based navigation in a collaborative environment (e.g. aircraft providing their navigation parameters through ADS-B) can expect robustness at a reasonable cost.
Keywords : UAS detect & avoid robustness navigation accuracy self-separation geometrical algorithm
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Communication dans un congrès
12th USA/Europe Air Traffic Management Research and Development Seminar, Jun 2017, Seattle, United States
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Soumis le : mardi 9 mai 2017 - 09:21:02
Dernière modification le : jeudi 23 août 2018 - 10:08:01
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Cyril Allignol, Nicolas Barnier, Nicolas Durand, Guido Manfredi, Éric Blond. Assessing the Robustness of a UAS Detect & Avoid Algorithm. 12th USA/Europe Air Traffic Management Research and Development Seminar, Jun 2017, Seattle, United States. 〈hal-01519659〉

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