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Autonomous unpowered flight is a challenge for control and guidance systems:all the energy the aircraft might use during flight has to be harvesteddirectly from the atmosphere. We investigate the design of an algorithm thatoptimizes the closed-loop control of a glider's bank and sideslip angles, whileflying in the lower convective layer of the atmosphere in order to increase itsmission endurance. Using a Reinforcement Learning approach, we demonstrate thepossibility for real-time adaptation of the glider's behaviour to thetime-varying and noisy conditions associated with thermal soaring flight. Ourapproach is online, data-based and model-free, hence avoids the pitfalls ofaerological and aircraft modelling and allow us to deal with uncertainties andnon-stationarity. Additionally, we put a particular emphasis on keeping lowcomputational requirements in order to make on-board execution feasible. Thisarticle presents the stochastic, time-dependent aerological model used forsimulation, together with a standard aircraft model. Then we introduce anadaptation of a Q-learning algorithm and demonstrate its ability to control theaircraft and improve its endurance by exploiting updrafts in non-stationaryscenarios.

Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring