Neural-Network-Based Path Replanning for Gliding Vehicles Considering Terminal Velocity

A data-driven path replanning algorithm based on Pythagorean-hodograph (PH) curve is proposed for unpowered gliding vehicles considering the terminal velocity constraint in uncertain windy environments. Based on the characteristics of the PH curve, the terminal velocity constraint is satisfied by adjusting path parameters including the arc length in path replanning. For the path replanning, a tailored PH curve regeneration algorithm is proposed to resolve the inconsistent path replanning issue of the existing PH curve generation method. An artificial-neural-network-based path replanner is trained to provide the path parameters corresponding to the terminal velocity constraint. Unlike most model-based methods, the proposed method can deal with the external disturbance by replanning the flight path. Compared with other data-driven studies, the proposed method does not require a computationally expensive trajectory optimization process to collect training data. Performance evaluation and comprehensive comparison are performed via numerical simulation considering various types of modeled and unmodeled wind uncertainties using several network configurations.