Separation Assurance in Urban Air Mobility Systems using Shared Scheduling Protocols

TL;DR

The paper addresses the challenge of ensuring safe separation in dense urban air mobility (UAM) networks by coordinating access to intersection bottlenecks through decentralized shared scheduling protocols within a dec-MDP framework. It proposes three fully decentralized protocols (CSMA/CD, Shortest Remaining Time First, and a Round Robin variant) plus a baseline, enabling aircraft to adjust speed or halt to maintain $d_{LOS}$ while sharing limited intersection airspace. Simulations in BlueSky with six routes and two intersections show that all scheduling protocols can drive LOS violations to zero, but at the cost of longer flight times as traffic density rises, with CSMA/CD typically most robust to non-compliant aircraft. The work demonstrates a scalable, training-free approach to real-time separation assurance that can complement strategic planning and CD&R methods, and it outlines avenues for integrating with demand-capacity balancing and reinforcement-learning-based refinements for improved efficiency and robustness.

Abstract

Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.

Figures (6)

• Figure 1: Illustration of a UAM flight network with intersecting corridors and aircraft positions. The flight corridors $\phi^i_1, \phi^i_2, \phi^i_3$ (blue) and $\phi^j_1, \phi^j_2, \phi^j_3$ (red) represent different routes for aircraft $i$ and $j$. The intersection, shown as a gray circle with green corridors, occurs where these corridors converge. Aircraft $i$ and $j$ are positioned inside and outside the intersection, respectively.
• Figure 2: Simulation setting used for tests, consisting of 6 routes and 2 intersections in the BlueSky air traffic simulator. We evaluate shared scheduling protocols in this scenario, varying traffic densities to assess their performance under different conditions.
• Figure 3: Average Number of LOS events in Scenarios with increasing numbers of aircraft per route. All shared scheduling protocols reduce LOS events to 0 even as the number of aircraft increases.
• Figure 4: Average Maximum Flight Times in Scenarios with Increasing Numbers of Aircraft. While protocols can reduce LOS events to 0, we note an increase in aircraft flight times.
• Figure 5: Number of LOS events at intersections between compliant and non-compliant aircraft. These results are normalized by the number of compliant aircraft. CSMA/CD shows the best performance in settings with high percentages of non-compliant aircraft.

Theorems & Definitions (11)

• Definition 1
• Definition 2: Observation Sequence
• Definition 3: Critical Resource
• Definition 4: Blocking
• Definition 5: Pairwise Bottlenecks
• Definition 6: Collection of Bottlenecks
• Definition 7: Scheduling Protocol
• Definition 8: Flight Corridor
• Definition 9: Routes
• Definition 10: Intersecting Flight Corridors