Wind-energy based path planning for Unmanned Aerial Vehicles using Markov Decision Processes

Exploiting wind-energy is one possible way to extend the flight duration of an Unmanned Aerial Vehicle. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain, time-varying wind fields and plan a path through them that exploits the energy the field provides. A Gaussian distribution is used to determine uncertainty in the time-varying wind fields. We use a Markov Decision Process to plan a path based upon the uncertainty of the Gaussian distribution. Simulation results are presented to compare the direct line of flight between a start and target point with our planned path for energy consumption and time of travel. The result of our method is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.