Abstract
The key challenge to integrate the unmanned aircraft system (UAS) into airspace is to develop a means to sense and avoid (SAA) other aircrafts. The main function of the SAA is self-separation, i.e. remaining “well clear” of the other aircrafts. The separation thresholds must be quantitatively defined for the UAS to autonomously maintain self-separation. In this paper, the separation thresholds are defined quantitatively for the UAS in a dynamic airspace full of aircrafts that differ in motion state and performance. Then, a “sector-like” dynamic collision-free region (CFR) was set up around the UAS. The size of the CRF can be adjusted adaptively according to the relative motion states of the surrounding intruders, the performance of the UAS, and the altitude of the airspace. The simulation results show that the proposed adaptive separation thresholds adapted to the dynamic airspace environment better than the fixed separation thresholds recommended by Sense & Avoid Science and Research Panel (SARP), and controlled the missing and false alarm rates on low levels. This means our adaptive separation thresholds can effectively balance the safety and operation efficiency of the airspace.
Highlights
With the development of artificial intelligence and aircraft industry, unmanned aircraft systems (UAS), a.k.a. unmanned aerial vehicles (UAVs), have been growing in quantity and diversity in recent years [1]
The analysis shows that the adaptive separation thresholds greatly outperformed the fixed separation thresholds
In this paper, a separation threshold computing method is developed for the UAS to autonomously maintain self-separation in dynamic airspace environment
Summary
With the development of artificial intelligence and aircraft industry, unmanned aircraft systems (UAS), a.k.a. unmanned aerial vehicles (UAVs), have been growing in quantity and diversity in recent years [1]. In the low-altitude dynamic airspace, using the traditional fixed separation thresholds, the UAS either fails to detect the approaching intruder in time or makes too many unnecessary collision avoidance maneuvers, which lowers the operation efficiency and safety in this type of airspace. The intruder Pint has a medium threat to the UAS, if it is outside AM and BN (δ ∈ [β, θ4] U [−θ4, −β]) In this case, the separation is mainly maintained by the lateral safety boundaries of the CFR. The value of kMR can be obtained from the coordinates of point M (xM , yM ), which depends on the angular range β of the ‘‘sector-like’’ CFR, and the minimum distance (rsmaifne) between the UAS and line AM. The UAS will issues a CA, and the UAS will adjust its path to maintain the separation from surrounding traffic
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