Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning
For advanced air mobility operations, this work provides a simulation-based evaluation framework for conflict resolution strategies under realistic degraded surveillance conditions, though it is an incremental application of existing methods.
This study develops a Deep Q-Network-based Multi-Agent Reinforcement Learning framework for decentralized conflict resolution among heterogeneous aircraft in air corridors under degraded surveillance. The framework achieves loss-of-separation resolution within 1 second for most events, with agents maintaining motion 79% of the time under safe conditions.
Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporarily unavailable. This study develops a Deep Q-Network-based Multi-Agent Reinforcement Learning framework for decentralized conflict resolution among heterogeneous small unmanned aerial vehicles and electric vertical takeoff and landing aircraft operating within a structured three-dimensional corridor. Separate policies are trained for the two aircraft categories using local observations and a 14-action space that includes maintaining course, turning, vertical maneuvering, landing, and speed control. The simulation incorporates aircraft-specific dynamics, energy use, corridor constraints, observation noise, communication delay, information dropout, wind disturbance, actuator uncertainty, and model uncertainty. The trained policies are evaluated across 90 combinations of traffic density and minimum separation thresholds. Loss-of-separation frequency and duration generally increase with traffic density and separation requirements, although most events are resolved within 1s. Under safe conditions, agents maintain their motion approximately 79% of the time. During conflicts, turning accounts for 33% of actions, followed by maintaining motion at 29%, speed control at 25%, and vertical maneuvers at 13%. Six Pareto-optimal configurations reveal trade-offs between safety and corridor capacity. The framework supports the simulation-based evaluation of safer AAM conflict-resolution strategies under degraded surveillance conditions.