Towards Reduced-Order Models for Online Motion Planning and Control of UAVs in the Presence of Wind

This paper describes a model reduction strategy for obtaining a computationally efficient prediction of a fixed-wing UAV performing waypoint navigation under steady wind conditions. The strategy relies on the off-line generation of time parametrized trajectory libraries for a set of flight conditions and reduced order basis functions functions for determining intermediate locations. It is assumed that the UAV has independent bounded control over the airspeed and altitude, and consider a 2D slice of the operating environment. We found that the reduced-order model finds intermediate positions within 10% and at speeds of 10x faster than clock-time (even in wind conditions in excess of 50% of the UAV’s forward airspeed) when compared against simulation results using a medium-fidelity flight dynamics model. The potential of this strategy for online planning operations is highlighted.