Deep learning for unmanned aerial vehicles landing carrier in different conditions

With the rapid development of unmanned aerial vehicles (UAVs) technology, it is necessary to ensure the safe and stable landing on the carrier. In this paper, we present a deep learning for UAVs Landing carrier in different conditions. Firstly it analysis of different sea conditions deck motion, constructs a simulation model of the system. The waves motion, deck motion, and then to the aircraft landing motion models are simulation. Then according to a large number of previous land runway, mobile landing platform experimental data, UAV model, wind model and deck motion model build aircraft carrier simulation system. That is based on deep learning, to estimate the safety conditions of contact carrier aircraft and deck. Then, it simulate the deck in different sea conditions and wave motion under the harsh conditions of motion, to testing the deck can withstand the landing limit to make feasibility analysis. Finally, using there simulation data use for UAVs landing on river platform. Simulation results show that the sea conditions with effective longitudinal deviation and lateral deviation of the ship is the most significant. Level 4, level 5, and level 6 sea conditions idea landing condition success rate are 98%, 70.8%, 63%. And it successful uses for landing in river platform.