Unmanned air system search and localization guidance using reinforcement learning
Requirments for current and future Unmanned Air Vehicles with longer flight endurances have led to a need for an autonomous soaring capability. This paper investigates aircraft flight path guidance for search and localization of Regions of Interest, consisting of atmospheric phenomena. The problem is posed as an offine agent learning problem, of localizing atmospheric thermal locations and then guiding an Unmanned Air Vehicle to soar from one to another. Q- learning is used as the learning algorithm. The computational navigation solution used here is a basic grid algorithm that assigns thermal locations and intensities, with the representation being specified states, actions, goals, and rewards that are used to accomplish the agent learning. The approach is validated with a simulation of a powered Unmanned Air Vehicle. Results presented in the paper show that the autonomous agent can learn how to navigate to a thermal quickly and effciently by controlling bank angle, while simultaneously monitoring its inertial position and heading angle. 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.