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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.
Shin et al. (Wed,) studied this question.