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(searched for: doi:10.1177/0954410019875241)
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, Luis Alvarez, Michael Owen, Benjamin Zintak
Journal of Air Transportation, Volume 30, pp 37-48; https://doi.org/10.2514/1.d0260

Abstract:
The capability to avoid other air traffic is a fundamental component of the layered conflict management system to ensure safe and efficient operations. The evaluation of systems designed to mitigate the risk of midair collisions of manned aircraft is based on large-scale modeling and simulation efforts and a quantitative volume defined as a near midair collision. Six-degree-of-freedom rigid point mass simulations are routinely employed by standards developing organizations when designing these systems. Because midair collisions are difficult to observe in these simulations and are inherently rare events, basing evaluations on near midair collisions enables a more robust statistical analysis. However, a near midair collision and its underlying assumptions for assessing close encounters with manned aircraft do not adequately consider the different characteristics of smaller drone encounters. The primary contribution of this paper is a quantitative criterion to use when simulating two or more smaller drones in sufficiently close proximity that a midair collision might reasonably occur and without any mitigations to reduce the likelihood of a midair collision. The criteria assume a historically motivated upper bound for the collision likelihood. We also demonstrate that the near midair collision analogs can be used to support modeling and simulation activities.
Published: 9 January 2022
by MDPI
Applied Sciences, Volume 12; https://doi.org/10.3390/app12020610

Abstract:
Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.
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