Real-Time Environmental Forecasting For Autonomous Aircraft

Abstract

The research intends to examine the feasibility of predicting a ship’s environmental conditions in real time in order to maximize the efficiency and safety of landing autonomous aircraft on its deck. The ship state is represented by 2 main axes: Roll and Pitch. The study will deal with predicting these 2 axes a few seconds ahead, which will allow landing on the ship more safely. According to conversations with pilots, and after looking at accidents that occurred while landing helicopters on ships, there seems to be a real need to increase safety conditions when making manned or autonomous landings. The research will include the development of an artificial intelligence platform that will enable forecasting the pitch and roll conditions on deck. The forecast data of the ship’s position will be one of the main factors to be transmitted in real time to the aircraft; knowledge of the ship’s immediate and future position will facilitate and ensure a soft landing of the aircraft on its deck. The ability to predict the ship’s future conditions will equip the ship and the drone with a technological advantage, as the platform will enable the aircraft to plan its landing and perform it more safely.