Model predictive collision avoidance and in-flight trajectory optimization considering UAM operation corridor

This paper proposes a linear model predictive control algorithm for obstacle avoidance and trajectory optimization based on Extended Kalman Filter to predict the motion of moving obstacles. In the actual operational environment of urban air mobility, it is difficult and impractical to consider various scenarios and apply them in advance when generating mission trajectories. Therefore, to provide real-time solutions for unforeseen situations, we perform trajectory optimization through linear model predictive control taking into account the dynamic constraints of UAM vehicles. By offering UAM flexible trajectories and executing obstacle avoidance, we can confirm the ability to avoid obstacles regardless of their approach at any given position.