Improving Safety of Vertical Manoeuvres in a Layered Airspace with Deep Reinforcement Learning

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Abstract

Current estimates show that the presence of unmanned aviation is likely to grow exponentially over the course of the next decades. Even with the more conservative estimates, these expected high traffic densities require a re-evaluation of the airspace structure to ensure safe and efficient operations. One structure that scored high on both the safety and efficiency metrics, as defined by the Metropolis project, is a layered airspace, where aircraft with an intended heading are assigned to a specific altitude layer. However, a problem arises once aircraft start to vertically traverse between these layers, leading to a large number of conflicts and intrusions. One way to potentially reduce the number of intrusions during these operations is by using conventional conflict resolution algorithms. These algorithms however have also been shown to lead to instabilities at higher traffic densities. As recent years have shown tremendous growth in the capabilities of Deep Reinforcement Learning, it is interesting to see how well these methods perform in the field of conflict resolution. This research investigates and compares the performance of multiple Soft Actor Critic models with the Modified Voltage Potential algorithm during vertical manoeuvres in a layered airspace. The final obtained performance of the trained models is comparable to that of the Modified Voltage Potential algorithm and in certain scenarios, the trained models even outperform the MVP algorithm. Overall, the results show that DRL can improve upon the current state of conflict resolution algorithms and provide new insight into the development of safe operations.
Original languageEnglish
Title of host publicationInternational Conference on Research in Air Transportation (ICRAT) 2022
Number of pages9
Publication statusPublished - 2022
Event10th International Conference for Research in Air Transportation - University of South Florida, Tampa, United States
Duration: 19 Jun 202223 Jul 2022
Conference number: 10
https://www.icrat.org/previous-conferences/10th-international-conference/

Conference

Conference10th International Conference for Research in Air Transportation
Abbreviated titleICRAT 2022
Country/TerritoryUnited States
CityTampa
Period19/06/2223/07/22
Internet address

Keywords

  • Keywords—Conflict Detection and Resolution (CD&R)
  • Deep Reinforcement Learning (DRL),
  • Modified Voltage Potential (MVP)
  • Unmanned Traffic Management (UTM)
  • Self-Separation
  • BlueSky ATC Simulator

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