06.1 - Flight Dynamics and Control (Control & Modelling)
PHYSICS-INFORMED LEARNING-BASED RECOVERY TRAJECTORY PREDICTION FOR AUTOMATIC GROUND COLLISION AVOIDANCE
J. Han¹, S. Kim¹, M. Jung¹, J. Woo¹, H.-S. Kim, APPIA Engineering Co., Ltd., South Korea; H. Bang¹; ¹Korea Advanced Institute of Science and Technology, South Korea
This paper presents a supervised learning–based approach for predicting recovery trajectories in Automatic Ground Collision Avoidance Systems. Motivated by accurate trajectory representation for timely maneuver initiation, the proposed method learns a parametric model from recovery maneuver data. The results demonstrate trajectory prediction accuracy across representative flight conditions.
DOI: 10.71945/icas2026_0865