具身被动气动声学感知实现空中机器人间的相对感知与追踪
Embodied Passive Aeroacoustic Perception Enables Relative Sensing and Pursuit Between Aerial Robots
浏览论文内容
中文总结 AI 辅助
本文提出具身被动气动声学感知范式，开发SonicFly框架，利用无人机固有飞行声音实现无额外设备的相对感知与追踪，室外实验获1.34米平均距离保持误差，验证该感知方法的可行性。
中文摘要 AI 辅助
空中机器人飞行时会产生结构化的气动声场，但这类信号作为机上相对感知源的应用尚未得到充分探索，尤其是在多种室外条件下同时飞行时产生的强烈自身声学干扰环境中。本文提出具身被动气动声学感知，即空中机器人在自身不断演变的气动声场中运行时，从自然产生的飞行声音中推断可操作的相对状态信息的感知范式。本文介绍SonicFly，一种被动气动声学感知框架，该框架使一架无人机仅利用另一架无人机的固有飞行声音，就能估计并追踪其位置，无需主动声学信号、机器人间通信、GPS共享或外部感知基础设施。该系统采用轻量化四麦克风阵列、旋翼机相关的声学表示、神经方位-距离估计器以及用于闭环飞行的置信门控滤波。通过声学表征、机上定位和室外追踪实验，我们证明多旋翼气动声学信号包含足够信息，可在强烈自身声学干扰、环境变化和飞行几何改变的情况下支持相对感知。在纯声学追踪中，SonicFly在不同室外轨迹和运行条件下实现平均距离保持误差为1.34米。对声学通道的分析揭示了具身被动气动声学感知的设计原则，包括谐波结构、光谱可分性和空间声学线索在确定可观测性中的作用。我们的结果确立了空中机器人具身被动气动声学感知的可行性，并表明自然产生的行为信号可作为机器人感知与协调的信息源。
英文摘要
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.
发表机构
- Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。
