Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162776
Title: Towards greener airport surface operations: a reinforcement learning approach for autonomous taxiing
Authors: Tran, Thanh-Nam
Pham Duc-Thinh
Alam, Sameer
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Civil engineering::Transportation
Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Issue Date: 2022
Source: Tran, T., Pham Duc-Thinh & Alam, S. (2022). Towards greener airport surface operations: a reinforcement learning approach for autonomous taxiing. International Workshop on ATM/CNS (IWAC 2022).
Abstract: 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 which is solved by training the agent under a Deep Reinforcement Learning (DRL) mechanism, using Proximal Policy Optimization (PPO) algorithm. The reward function is designed to consider the fuel burn, taxi-time, and delay-time. Thus, the trained agent will learn to taxi the aircraft between any pair of locations on the airport surface timely while maintaining safety and efficiency. As the result, in more than 97.8% of the evaluated sessions, the controlled aircraft can reach the target position with the time difference within the range of [-20,5] seconds. Moreover, compared with actual fuel burn, the proposed autonomous taxi-agent demonstrated a reduction of 29.5%, equivalent to the reduction of 13.9 kg of fuel 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.
URI: https://hdl.handle.net/10356/162776
URL: https://www.jstage.jst.go.jp/browse/-char/en
Rights: © 2022 Electronic Navigation Research Institute (ENRI). All rights reserved. This paper was published in the Proceedings of International Workshop on ATM/CNS (IWAC 2022) and is made available with permission of Electronic Navigation Research Institute (ENRI).
Fulltext Permission: embargo_20231130
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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