Flying Like a Pilot in Wind: Smooth Trajectory Optimization in a Moving Reference Frame

Abstract

A significant challenge for unmanned aerial vehicles capable of flying long distances is planning in a wind field. Although there has been a plethora of work on the individual topics of planning long routes, smooth trajectory optimization and planning in a wind field, it is difficult for these methods to scale to solve the combined problem. In this thesis, we address the problem of planning long, dynamically feasible, time-optimal trajectories in the presence of wind (which creates a moving reference frame). Additionally, we attempt to solve for trajectories that exhibit features similar to the flight profiles of human pilots.

We present an algorithm, kITE, that elegantly decouples the joint trajectory optimization problem into individual path optimization in a fixed ground frame and a velocity profile optimization in a moving reference frame. The key idea is to derive a decoupling framework that guarantees feasibility of the final fused trajectory. Our results show that kITE produces high-quality solutions for planning with a full-size helicopter flying at speeds of 50 m/s, handling winds up to 20 m/s and missions over 200 km. We validate our approach with real-world experiments on a full-scale helicopter with a pilot in the loop. Our approach paves the way forward for autonomous systems to exhibit pilot-like behavior when flying missions in winds aloft.