Aircraft AI Static Path Planning on Airport Ground Based on Reinforcement Learning

Abstract

With the rapid development of artificial intelligence(AI) and the proposal of "smart airport", it is of great importance to actively explore the application of AI in airports to assist airport controllers and pilots to command aircraft to taxiing on the aircraft ground effectively.A taxiing path planning method based on reinforcement learning is proposed, a reinforcement learning mobile model of aircraft airport is constructed, and then Meilan Airport of Haikou is taken as an example to achieve the scene simulation by using the Python built-in toolkit Tkinter.Considering the aircraft taxiing rules of the airport, the Q-Learning algorithm in Off-policy is used to solve the Bellman equation to realize the AI static path planning of aircraft in the model-based environment.The results show that the proposed method can realize the AI static path planning of aircraft from gate position to runway exit.

Keywords

• reinforcement learning
• q-learning algorithm
• aircraft taxiing rules
• ai static path planning