Drones (May 2023)

Parametric Study of Structured UTM Separation Recommendations with Physics-Based Monte Carlo Distribution for Collision Risk Model

  • Chung-Hung John Wang,
  • Chao Deng,
  • Kin Huat Low

DOI
https://doi.org/10.3390/drones7060345
Journal volume & issue
Vol. 7, no. 6
p. 345

Abstract

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With the increasing demand for unmanned aircraft system (UAS) traffic management (UTM) airspace comes the need to ensure the safe operation and management of said airspace. One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assurance. This could be achieved by establishing the separation requirements for the UTM traffic based on the flight dynamics and communication navigation surveillance (CNS) performance that could be achieved in the airspace in question. A modified Reich collision risk model, typically used in civil aviation for separation minima evaluation, was used for the evaluation of the initial separation that would meet the target level of safety within a prescribed look-ahead time. This paper presents the parametric evaluation of using this physics-based and Monte Carlo-driven Reich collision risk model to evaluate the separation recommendation needed to achieve 10−7 mid-air-collision risk in UTM. The evaluation was conducted for an encounter pair consisting of identical ∼1.2 kg quadrotors with various encounter geometries, cruise velocities, navigation uncertainties, and communication latency.

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