Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162776
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dc.contributor.authorTran, Thanh-Namen_US
dc.contributor.authorPham Duc-Thinhen_US
dc.contributor.authorAlam, Sameeren_US
dc.date.accessioned2022-12-16T06:19:54Z-
dc.date.available2022-12-16T06:19:54Z-
dc.date.issued2022-
dc.identifier.citationTran, 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).en_US
dc.identifier.urihttps://hdl.handle.net/10356/162776-
dc.description.abstractThis 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.en_US
dc.description.sponsorshipCivil Aviation Authority of Singapore (CAAS)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.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).en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.subjectEngineering::Civil engineering::Transportationen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Simulation and modelingen_US
dc.titleTowards greener airport surface operations: a reinforcement learning approach for autonomous taxiingen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.conferenceInternational Workshop on ATM/CNS (IWAC 2022)en_US
dc.contributor.researchAir Traffic Management Research Instituteen_US
dc.description.versionSubmitted/Accepted versionen_US
dc.identifier.urlhttps://www.jstage.jst.go.jp/browse/-char/en-
dc.subject.keywordsAutonomous Taxien_US
dc.subject.keywordsReinforcement Learningen_US
dc.subject.keywordsFuel Burnen_US
dc.subject.keywordsOptimal Speeden_US
dc.citation.conferencelocationTokyo, Japanen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme.en_US
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