Insects, bats, and small birds show outstanding flight performance even under complex atmospheric conditions, which is partially due to the ability of these natural fliers to sense and react to disturbances quickly. These biological systems often use large numbers of sensors arrayed across their bodies to detect disturbances, but previous efforts to use large arrays of sensors in engineered fliers have typically resulted in slow responses due to the need to scan and process data from the large number of sensors. To address the challenges of capturing disturbances in a large sensing array with low latency, this work proposes and demonstrates a modular soft sensing system to quickly detect disturbances in small unmanned aerial vehicles. A large array of soft strain sensors with high sensing resolution covers the entire wingspan, providing rich information on wing deformation. Owing to the modular design, decentralized computation enables the sensing system to efficiently manage sensor data, resulting in sufficiently fast sampling to capture wing dynamics while all 32 sensors embedded in the modular soft sensing skin are used. This hardware architecture also results in significantly reduced noise in the sensing system, leading to a high signal-to-noise ratio. These methods can ultimately enable fast and reliable control of both soft and rigid robotic systems using large arrays of soft sensors.

中文翻译：

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一种模块化的软传感蒙皮，用于快速测量小型无人机的机翼变形。


昆虫、蝙蝠和小鸟即使在复杂的大气条件下也表现出出色的飞行性能，这部分是由于这些自然飞行者能够快速感知干扰并做出反应。这些生物系统通常使用分布在身体上的大量传感器来检测干扰，但以前在工程飞行中使用大量传感器的努力通常会导致响应缓慢，因为需要扫描和处理来自大量传感器的数据。为了解决在大型传感阵列中以低延迟捕获干扰的挑战，这项工作提出并演示了一种模块化软传感系统，以快速检测小型无人机中的干扰。具有高传感分辨率的大量软应变传感器覆盖整个翼展，提供有关机翼变形的丰富信息。由于采用模块化设计，分散计算使传感系统能够有效地管理传感器数据，从而在使用嵌入模块化软传感皮肤中的所有 32 个传感器时，实现足够快的采样以捕获机翼动力学。这种硬件架构还显著降低了传感系统中的噪声，从而实现了高信噪比。这些方法最终可以使用大量软传感器对软机器人系统和刚性机器人系统进行快速可靠的控制。