This paper describes an Artificial Intelligence (AI) Deep Reinforcement Learning (DRL) model application to airport surface movement planning. Specific areas of support include planning conflict-free paths of flights, advising flight actions to avoid conflicts, and meeting flight-specific time constraints. We describe a unique way to represent the surface operational environment and schedule constraints for leveraging the DRL model with a Convolutional Neural Network and also demonstrate the training process and performance for routing surface traffic at a hypothetical airport. The proposed model learns entirely from the simulation to make sequential decisions for finding conflict-free routes, addressing incomplete schedule information, and coordinating the simultaneous rerouting of multiple flights. Such a DRL model, once adapted and trained for a realistic airport, may advise controllers on surface route planning, conflict probing, and resolving predicted conflicts.
Deep Reinforcement Learning Applied to Airport Surface Movement Planning
2019-09-01
2018957 byte
Conference paper
Electronic Resource
English
Aircraft AI Static Path Planning on Airport Ground Based on Reinforcement Learning
DOAJ | 2021
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