Learning to Have a Civil Aircraft Take Off under Crosswind Conditions by Reinforcement Learning with Multimodal Data and Preprocessing Data

Autopilot technology in the field of aviation has developed over many years. However, it is difficult for an autopilot system to autonomously operate a civil aircraft under bad weather conditions. In this paper, we present a reinforcement learning (RL) algorithm using multimodal data and preprocessing data to have a civil aircraft take off autonomously under crosswind conditions. The multimodal data include the common flight status and visual information. The preprocessing is a new design that maps some flight data by nonlinear functions based on the general flight dynamics before these data are fed into the RL model. Extensive experiments under different crosswind conditions with a professional flight simulator demonstrate that the proposed method can effectively control a civil aircraft to take off under various crosswind conditions and achieve better performance than trials without visual information or preprocessing data.