UAV Collision Risk Assessment in Terminal Restricted Area by Heatmap Representation

Abstract

The frequent intrusion of unmanned aerial vehicles (UAVs) into civil aviation airports poses a severe challenge to the safety management of the airport. The subsequent flight delays and even the closure of the runway have seriously affected the normal operation of the airport. To measure the collision risk of the Unmanned-Manned (U-M) encounter (intruding UAV to manned aircraft), a collision risk assessment platform is developed in this paper to predict the U-M encounter collision risk of the airport restricted area. The designed platform can be used to dynamically generate 3D trajectories of the U-M encounter, and to assess the collision risk with the changing of the relative distance in the probabilistic curve. Further, to predict the collision risk of the airport restricted area, the collision risk heatmap representation is then introduced. A case study of Changi Airport, Singapore is employed to generate the runway collision risk heatmap based on the designed collision risk assessment platform. The results demonstrate how the collision risk of the separate runway and the integrated runway distributes.

Geofencing Motion Planning for Unmanned Aerial Vehicles Using an Anticipatory Range Control Algorithm

“…Given the airspeed V, let s t in (12) equal Vt. In effect, after 3.7V seconds, the vehicle reaches (extremely close to) the maximum bank angle and is able to complete the turn with radius r. Clearly, (23) gives a trivial solution for ϕ → ϕ max (t = ∞), so a value sufficiently close to ϕ max (i.e., 99%) needs to be chosen.…”

“…However, since the turn was initiated before the vehicle left, the deviation is much less compared with a wholly reactive RTB approach. The deviations can be eliminated by increasing the value of t c and, hence, s t in (12), effectively increasing the responsiveness of the ARC to the approaching fence. In (23), t c = 1 s corresponds with ϕ = 0.713495ϕ max , and in (23), t c = 5 s corresponds with ϕ = 0.998070ϕ max , whereas t c = 10 s corresponds with ϕ = 0.999996ϕ max .…”

“…Comprehensive geofencing solutions will play an important part in future traffic management in urban environments and in the provision of autonomous advanced air mobility (AAM). There is much research ongoing on traffic planning [8][9][10], risk management [11] and collision avoidance [12,13] in this area.…”