Real-Time Planning and Control with a Vortex Particle Model for Fixed-Wing UAVs in Unsteady Flows

Abstract

Unsteady aerodynamic effects can have a profound impact on aerial vehicle flight performance, especially during agile maneuvers and in complex aerodynamic environments. In this paper, we present a real-time planning and control approach capable of reasoning about unsteady aerodynamics. Our approach relies on a lightweight vortex particle model, parallelized to allow GPU acceleration, and a sampling-based policy optimization strategy capable of leveraging the vortex particle model for predictive reasoning. We demonstrate, through both simulation and hardware experiments, that by replanning with our unsteady aerodynamics model, we can improve the performance of aggressive post-stall maneuvers in the presence of unsteady environmental flow disturbances.