Active propulsion noise shaping for multi-rotor aircraft localization
This addresses localization challenges for micro aerial vehicles in vision-impaired conditions, offering a low-cost, energy-efficient acoustic alternative, though it is incremental as it builds on existing acoustic sensing methods.
The paper tackles the problem of multi-rotor aircraft localization by actively shaping propulsion noise with neural networks and rotor phase modulation, achieving accurate and robust results in simulated 2D acoustic environments fitted to real data.
Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes. However, visual localization and odometry techniques suffer from poor performance in low or direct sunlight, a limited field of view, and vulnerability to occlusions. Acoustic sensing can serve as a complementary or even alternative modality for vision in many situations, and it also has the added benefits of lower system cost and energy footprint, which is especially important for micro aircraft. This paper proposes actively controlling and shaping the aircraft propulsion noise generated by the rotors to benefit localization tasks, rather than considering it a harmful nuisance. We present a neural network architecture for selfnoise-based localization in a known environment. We show that training it simultaneously with learning time-varying rotor phase modulation achieves accurate and robust localization. The proposed methods are evaluated using a computationally affordable simulation of MAV rotor noise in 2D acoustic environments that is fitted to real recordings of rotor pressure fields.