Embodied Passive Aeroacoustic Perception Enables Relative Sensing and Pursuit Between Aerial Robots

2026-07-31

AI Summary

This work proposes an embodied passive aeroacoustic sensing paradigm for outdoor environments devoid of GPS, communication, or external infrastructure, enabling a following drone to estimate its relative state and achieve closed-loop tracking solely by listening to the natural flight noise generated by a leader drone. The approach demonstrates for the first time that multicopter self-generated aeroacoustic signals can support relative perception without active sound emission or inter-agent communication. Leveraging a lightweight four-microphone array, rotor-specific acoustic representations, a neural network-based bearing-and-range estimator, and a confidence-gated filtering scheme, the system achieves an average distance-keeping error of 1.34 meters across diverse outdoor trajectories. The study further reveals the critical role of harmonic structure, spectral separability, and spatial acoustic cues in enabling observability under such passive sensing conditions.

Abstract

Aerial robots generate structured aeroacoustic fields during flight, yet these signals have been underexplored as a source of onboard relative perception, particularly under the strong ego-acoustic interference generated during simultaneous flight in various outdoor conditions. We introduce embodied passive aeroacoustic perception, a sensing paradigm in which an aerial robot infers actionable relative-state information from the naturally generated sound of flight while operating within its own evolving aeroacoustic field. We present SonicFly, a passive aeroacoustic perception framework that enables one unmanned aerial vehicle to estimate and follow another using only the leader's intrinsic flight sound, without active acoustic signaling, inter-robot communication, GPS sharing, or external sensing infrastructure. The system uses a lightweight four-microphone array, rotorcraft-informed acoustic representations, a neural bearing-range estimator, and confidence-gated filtering for closed-loop flight. Through acoustic characterization, onboard localization, and outdoor pursuit experiments, we show that multirotor aeroacoustic signals contain sufficient information to support relative perception despite strong ego-acoustic interference, environmental variability, and changing flight geometry. During acoustic-only pursuit, SonicFly achieved a mean distance-maintenance error of 1.34 m across diverse outdoor trajectories and operating conditions. Analysis of the acoustic channel reveals design principles governing embodied passive aeroacoustic perception, including the roles of harmonic structure, spectral separability, and spatial acoustic cues in determining observability. Our results establish the feasibility of embodied passive aeroacoustic perception for aerial robots and suggest that naturally generated behavioral signals can serve as information for robotic perception and coordination.

Problem

• aeroacoustic perception
• relative sensing
• aerial robots
• ego-acoustic interference
• passive sensing

Innovation

• passive aeroacoustic perception
• embodied sensing
• acoustic localization
• multirotor drones
• ego-acoustic interference