Deep Reinforcement Learning Applied to Airport Surface Movement Planning
| Autor: | Sheng Liu, Shin-Lai Alex Tien, Huang Tang, Erik Vargo, Daniel B. Kirk |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Surface (mathematics)
0209 industrial biotechnology Schedule Operations research Process (engineering) Computer science 02 engineering and technology Convolutional neural network 020901 industrial engineering & automation Model application 0202 electrical engineering electronic engineering information engineering Reinforcement learning 020201 artificial intelligence & image processing Routing (electronic design automation) Movement planning |
| Zdroj: | 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). |
| DOI: | 10.1109/dasc43569.2019.9081720 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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