Detect & Avoid, UAV Integration in the Lower Airspace Traffic

Cyril Allignol (1, 2) , Nicolas Barnier (1) , Nicolas Durand (1, 2) , Éric Blond (3)

1 MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
2 IRIT-APO - Algorithmes Parallèles et Optimisation
3 DTI - Direction de la technique et de l'innovation de la DGAC

In this article, we test a horizontal detect and avoid algorithm for UAVs flying in the lower airspace (under FL180). We use recorded commercial traffic trajectories and randomly build 3000 conflicts scenarios with UAVs to check the ability of such an algorithm to ensure the separation with commercial aviation. We consider two different types of UAVs, the first type flying at 80 kn and the second type flying at 160 kn with six different missions: flying straight or turning and leveled, climbing or descending. We only focus on horizontal maneuvers in order not to interfere with aircraft TCAS. The article investigates the influence of the various parameters on the separation achieved. The analysis of results from over 200 000 simulations provides minimum requirements on the frequency and anticipation time of the resolution process for an efficient detect and avoid strategy and brings up remaining issues in scenarios where the UAV has a low maneuverability and encounters fast airliners with changing speed and heading.

Mots clés: conflict resolution; detect and avoid; self-separation; UAV; geometrical algorithm

Domaines: Mathématiques [math]; Optimisation et contrôle [math.OC]; Informatique [cs]; Modélisation et simulation; Informatique [cs]; Intelligence artificielle [cs.AI]; Informatique [cs]; Algorithme et structure de données [cs.DS]; Informatique [cs]; Recherche opérationnelle [cs.RO]

Cyril Allignol, Nicolas Barnier, Nicolas Durand, Éric Blond. Detect & Avoid, UAV Integration in the Lower Airspace Traffic. ICRAT 2016, 7th International Conference on Research in Air Transportation, Jun 2016, Philadelphia, United States. ⟨hal-01351007⟩