A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management

TL;DR

This paper presents a multi-agent reinforcement learning method for managing urban air mobility traffic, focusing on balancing noise reduction and safety through altitude control in a complex urban environment.

Contribution

It introduces a novel reinforcement learning framework that simultaneously addresses noise mitigation and safety in UAM traffic management, a challenge not previously tackled with this approach.

Findings

Reinforcement learning effectively balances noise and safety in UAM traffic.

Altitude adjustments can mitigate noise while maintaining safe separation.

Tradeoffs exist between noise reduction, traffic congestion, and safety.

Abstract

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