Autonomous multi-rotor aerial vehicles (MAVs) are an emerging technology, which has a large number of current and potential applications in a wide range of industries. These airborne instruments are becoming growingly autonomous thanks to modern artificial intelligence technologies, with their navigation and interaction capabilities based predominantly on visual sensing. While vision has attracted considerable attention, it suffers from a poor performance in low light and direct sunlight conditions and is vulnerable to occlusions. This project targets to change this situation, endowing drones with “ears”. The proposed research aims at the development of novel algorithms based on a combination of machine learning and physical modeling, and real-time systems for acoustic-based autonomous mapping, localization, and interaction of MAVs.
The use of drones and UAVs has seen a large surge in recent years. Drones have been applied in new industries, including commercial and military applications. A major concern for this industry that is still to be addressed is the noise emission during flight. Small scale drones tend to use electrical motors that are quiter than other means of propulsion. This make the propeller the most prominent source of noise. Therefore, understanding and abatement propeller acoustic signature of small UAV rotor is crucial.
The high level of noise generated during multi rotor flight is a major concern for the aeronautical industry. In the case of civil applications, noise has a more comprehensive range of implications due to the sustainability of air traffic growth. Single rotor noise is a superposition of tonal and broadband components. The tonal component is composed of thickness and loading noise, high-speed impulsive noise and blade-vortex interaction noise. The broadband component is related with non-deterministic loading sources (e.g. blade-wake interactions and blade self-noise turbulence ingestion noise). Within the ASSURE projects our main objective is to collect, process and archive noise signature data from single/multiple number of small unmanned aerial systems (or UAS) rotors. The resulting archive of UASs noise data will assist FAA in developing standards and regulatory products, and will represent an invaluable source for further validations of analytical and numerical tools. The proposed project falls under “Unmanned Aircraft Systems (UAS) Noise Certification” FAA research requirement (UAS Research Focus Area: Airworthiness | A-8 UAS Noise Certification).
Requirements: Good knowledge of signal processing