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 vehicles are becoming growingly autonomous thanks to modern artificial intelligence technologies, with their navigation and interaction capabilities based predominantly on visual sensing. While vision navigation has attracted considerable attention, it suffers from a poor performance in low light, limited field of view, and direct sunlight and is vulnerable to occlusions. Acoustic sensing can complement and even replace vision in many situations, and it also benefits from lower system cost and energy footprint, which is especially important for small form-factor aircraft. While novel acoustic technologies based on phased microphone arrays are making their way into the Internet of Things and home automation markets, their use in MAVs is currently impeded by the strong self-noise generated by the drone propulsion system. Consequently, existing commercial and research aerial platforms have advanced vision capabilities, yet no acoustics. This project targets to change this situation, endowing drones with "ears". The proposed research aims at the development of novel machine learning-based algorithms and real-time systems for acoustic-based autonomous mapping, localization, and interaction of MAVs. One of the key ideas of the proposal consists of actively controlling and shaping the aircraft self-noise for the benefit of the navigation and interaction tasks, instead of considering it a harmful nuisance. Our end goal is to demonstrate a flying proof-of-concept system which, to the best of our knowledge, will be the first of its kind. While the primary goal of the project is very specific, achieving it will require a considerable amount of scientific and methodological innovation in modelling, signal processing, and machine learning that we expect to have a significant impact on broad domains.
