Demonstration of Prop-Rotor System Development for 52kg MTOW Quad-Tilt Prop UAV

Abstract

Small electric drones (UAVs) were introduced to public people and developed world widely. Companies more than 200 have developed various types of electric drones. Specially the multi-copter which has four propellers, called as quad-copter, was very popular in public people that enjoy personal leisure, sport and hobby. The drone’s technical tendency of worldwide market requested to increase payload, better hover performance and high forward flight speed gradually. Through world market's requirement and technical tendency, KARI had interests in electric drone (UAV) market such as the high flight speed electric drones which have hover and vertical take-off and landing (VTOL) capabilities. In special, the electric UAV which has the reconnaissance and observation mission gives a deep attention to government people, industries and military people. For these targets and goals, KARI had launched to develop 52kg Maximum Take-off Weight (MTOW) Quad-Tilt Prop UAV (QTP UAV) through the previous KARI's smart UAV, rotor system development experience and tilt-rotor technologies. The paper describes that the whole research activity works of its prop-rotor system for QTP UAV. KARI had developed some rotor systems for rotorcraft and UAV more than 20 years. This paper introduces the development of the multidisciplinary design and analysis optimization work for developing prop-rotor system of KARI QTP UAV by using optimization tools which cover better hover and forward flight performance. To research and develop the new prop-rotor system, the new aerodynamic and structural design of propeller had been conducted and these activities were described briefly. Finally, flight demonstration was shown to verify the performance of this prop-rotor system of QTP. For these whole development period, KARI had verified and shown the whole research and development works of the prop-rotor system which as a high performance for QTP UAV founded on main rotor system's previous knowledge and new optimization process.