Path planning for autonomous soaring flight in dynamic wind fields

Abstract

An autonomous aircraft capable of utilising soaring flight in a dynamic wind field could considerably extend flight duration by limiting the use of on-board energy for propulsion. While soaring flight is relatively well understood for known wind, an autonomous soaring aircraft would have to generate paths based only on local observations of the wind made during the flight. This paper presents a method to simultaneously map and utilise a wind field using Gaussian process regression to generate a spatio-temporal map of the wind, and a path planning and dynamic target assignment algorithm to generate energy-gain paths from the current wind estimate. The planning architecture is tested in simulation for dynamic wind fields and shows consistent energy gain through exploration and exploitation of the wind environment.