静かで安全な都市航空モビリティ交通管理のための強化学習アプローチ
A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management
都市航空モビリティ(UAM)の交通管理において、マルチエージェント強化学習を用いて高度調整を行い、騒音低減と航空機間の安全な間隔確保の両立を目指した研究。
著者: Surya Murthy, John-Paul Clarke, Ufuk Topcu, Zhenyu Gao
分類: cs.MA, cs.LG, cs.RO
原文アブストラクト
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
