摘要：安全的先进空中交通（Advanced Air Mobility，AAM）运行要求航空器在监视信息存在噪声、延迟、不完整或暂时不可用时仍能保持间隔。本研究开发了一个基于 Deep Q-Network 的多智能体强化学习框架，用于在结构化三维走廊内运行的异构小型无人机和电动垂直起降飞行器之间实现去中心化冲突解决。研究针对两类航空器分别训练策略，使用局部观测以及包含保持航向、转弯、垂直机动、着陆和速度控制在内的 14 动作空间。仿真纳入了航空器特定动力学、能源消耗、走廊约束、观测噪声、通信延迟、信息丢失、风扰动、执行器不确定性和模型不确定性。训练完成的策略在交通密度与最小间隔阈值的 90 种组合下进行了评估。失去间隔事件的频率和持续时间通常会随交通密度及间隔要求提高而增加，但大多数事件可在 1 秒内解决。在安全条件下，智能体约有 79% 的时间保持原有运动状态。发生冲突时，转弯占动作的 33%，其次是保持运动状态（29%）、速度控制（25%）和垂直机动（13%）。六种 Pareto 最优配置揭示了安全性与走廊容量之间的权衡。该框架支持在降级监视条件下，通过仿真评估更安全的 AAM 冲突解决策略。

Abstract: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.