Towards Greener Airport Surface Operations: A Reinforcement Learning Approach for Autonomous Taxiing

抄録

This study proposes an autonomous aircraft taxi agent that can be used to recommend the pilot the optimal speed profile to achieve optimal fuel burn and to arrive on time at the target position on the taxiway while considering potential interactions with surrounding traffic. The problem is modeled as a control decision problem that is solved by training the agent under a Deep Reinforcement Learning (DRL) mechanism using the Proximal Policy Optimization (PPO) algorithm. The reward function is designed to consider the fuel burn, taxi time, and delay time. Accordingly, the trained agent will learn to taxi the aircraft between any pair of locations on the airport surface in a timely manner while maintaining safety and efficiency. As a result, in more than 97.8% of the evaluated sessions, the controlled aircraft reached the target position with a time difference falling within the range of −20 to 5 s. Moreover, compared to actual fuel burn, the proposed autonomous taxi agent demonstrated a reduction of 29.5%, equivalent to reducing 13.9 kg of fuel consumption per aircraft. This benefit in fuel burn reduction can complement the emission reductions achieved by solving other sub-problems, such as pushback control and taxi-route assignments, to achieve much higher performance.