用强化学习优化城市空中交通，兼顾安静与安全
A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management
- 多智能体强化学习通过调整飞行高度实现分层管理
- 在降噪与保持安全距离间取得平衡，缓解交通拥堵
- 适合关注智能空管与低噪音飞行的从业者
城市空中交通（UAM）是一种变革性系统，利用小型飞行器在城市环境中重塑交通方式。然而，将UAM融入现有城市环境面临诸多复杂挑战。近期研究指出，飞机噪声与系统安全是制约UAM实施的关键障碍。未来的UAM空管方案必须兼顾安静与安全。本文提出一种多智能体强化学习方法，用于管理UAM交通，旨在实现垂直间隔保障与噪声抑制。通过大量训练，强化学习代理学会在多层UAM网络中通过高度调整来权衡两大核心目标。结果揭示了噪声影响、交通拥堵与间隔之间的权衡关系。总体而言，研究证明强化学习可通过高度调整有效缓解UAM噪声问题，同时维持安全间隔。
原文摘要 · Abstract (English)
Urban air mobility (UAM) is a transformative system that operates various small aerial vehicles in urban environments to reshape urban transportation. However, integrating UAM into existing urban environments presents a variety of complex challenges. Recent analyses of UAM's operational constraints highlight aircraft noise and system safety as key hurdles to UAM system implementation. Future UAM air traffic management schemes must ensure that the system is both quiet and safe. We propose a multi-agent reinforcement learning approach to manage UAM traffic, aiming at both vertical separation assurance and noise mitigation. Through extensive training, the reinforcement learning agent learns to balance the two primary objectives by employing altitude adjustments in a multi-layer UAM network. The results reveal the tradeoffs among noise impact, traffic congestion, and separation. Overall, our findings demonstrate the potential of reinforcement learning in mitigating UAM's noise impact while maintaining safe separation using altitude adjustments
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