Nano-drones, distinguished by their agility, minimal weight, and cost-effectiveness, are particularly well-suited for exploration in confined, cluttered and narrow spaces. Recognizing transparent, highly reflective or absorbing materials, such as glass and metallic surfaces is challenging, as classical sensors, such as cameras or laser rangers, often do not detect them. Inspired by bats, which can fly at high speeds in complete darkness with the help of ultrasound, this paper introduces \textit{BatDeck}, a pioneering sensor-deck employing a lightweight and low-power ultrasonic sensor for nano-drone autonomous navigation. This paper first provides insights about sensor characteristics, highlighting the influence of motor noise on the ultrasound readings, then it introduces the results of extensive experimental tests for obstacle avoidance (OA) in a diverse environment. Results show that \textit{BatDeck} allows exploration for a flight time of 8 minutes while covering 136m on average before crash in a challenging environment with transparent and reflective obstacles, proving the effectiveness of ultrasonic sensors for OA on nano-drones.

翻译：摘要：纳米无人机以其灵活性高、重量轻、成本效益显著等特点，特别适用于在封闭、杂乱及狭窄空间中进行探索。然而，识别透明、高反射或吸波材料（如玻璃和金属表面）具有挑战性，因为传统传感器（如摄像头或激光测距仪）常难以检测到这些材料。受蝙蝠在完全黑暗中借助超声波高速飞行的启发，本文提出了一种名为《BatDeck》的革新性传感器模块，该模块采用轻量级、低功耗的超声波传感器，实现纳米无人机的自主导航。本文首先解析传感器特性，着重阐述电机噪声对超声波读数的干扰，随后介绍了在多样化环境中进行避障（OA）的广泛实验测试结果。结果表明，在包含透明及反射障碍物的复杂环境中，《BatDeck》可使纳米无人机在碰撞前平均飞行8分钟、覆盖136米距离，证实了超声波传感器在纳米无人机避障中的有效性。