A path planning method based on Q-Learning was proposed to address the issue of low accuracy in aircraft taxi path planning based on traditional algorithms and inability to plan path according to overall surface operation.By analyzing the network structure model of the airport flight zone and the simulated environment of reinforcement learning,the state space and action space were set,and the reward function was set based on the compliance and rationality of the path,and the evaluation value of the path rationality was set as the reciprocal of the product of the length of the taxi path and the average taxi time in the flight zone.Finally,the impact of parameters of action selec-tion strategy on the path planning model was analyzed.The results showed that compared with the A*algorithm and Floyd algorithm,path planning based on Q-learning can avoid relatively busy areas while minimizing the taxi dis-tance,resulting in high evaluation values of path rationality.
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