(searched for: doi:10.1177/0954410019875241)
Drones, Volume 6; https://doi.org/10.3390/drones6070157
A zoning approach that divides an area of interest into multiple sub-areas can be a systemic and strategic solution to safely deploy a fleet of unmanned aerial vehicles (UAVs) for package delivery services. Following the zoning approach, a UAV can be assigned to one of the sub-areas, taking sole ownership and responsibility of the sub-area. As a result, the need for collision avoidance between units and the complexity of relevant operational activities can be minimized, ensuring both safe and reliable execution of the tasks. Given that the zoning approach involves the demand-server allocation decision, the service quality to customers can also be improved by performing the zoning properly. To illuminate the benefits of the zoning approach to UAV operations from a systemic perspective, this study applies clustering techniques to derive zoning solutions under different scenarios and examines the performance of the solutions using a simulation model. The simulation results demonstrate that the zoning approach can improve the safety of UAV operations, as well as the quality of service to demands.
Published: 1 April 2022
Journal of Air Transportation, Volume 30, pp 37-48; https://doi.org/10.2514/1.d0260
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.
Applied Sciences, Volume 12; https://doi.org/10.3390/app12020610
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.