<p>Distributed pressure sensor array is a promising approach for the estimation of flight parameters for small unmanned aerial vehicles. Current flexible pressure sensor arrays are conventionally subjected to limited sensor resolution, poor bending flexibility, and inadequate packaging protection, resulting in insufficient precision for flight parameter estimation. Here we present a high-resolution differential pressure sensor array using a calorimetric measurement method and a multilayer polyimide bonding technique. The proposed differential pressure sensor array reaches a detection limit of 36.5 mPa over a range of 500 Pa and shows high repeatability when attached to varying curved surfaces. In addition, a superhydrophobic packaging is integrated into the sensor fabrication process, endowing it with waterproof capability. Utilizing a multilayer perceptron neural network, we demonstrated the function of the sensor array in estimating airspeeds and angle of attacks, achieving average solving errors of 0.15 m/s and 0.37°, respectively.</p>

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Flexible calorimetric differential pressure sensor array integrated with a superhydrophobic surface for flight parameter estimation

  • Xin Ke,
  • Yu Gao,
  • Zheng Gong,
  • Yunfan Li,
  • Zihao Dong,
  • Bowei Wan,
  • Yurun Guo,
  • Yonggang Jiang

摘要

Distributed pressure sensor array is a promising approach for the estimation of flight parameters for small unmanned aerial vehicles. Current flexible pressure sensor arrays are conventionally subjected to limited sensor resolution, poor bending flexibility, and inadequate packaging protection, resulting in insufficient precision for flight parameter estimation. Here we present a high-resolution differential pressure sensor array using a calorimetric measurement method and a multilayer polyimide bonding technique. The proposed differential pressure sensor array reaches a detection limit of 36.5 mPa over a range of 500 Pa and shows high repeatability when attached to varying curved surfaces. In addition, a superhydrophobic packaging is integrated into the sensor fabrication process, endowing it with waterproof capability. Utilizing a multilayer perceptron neural network, we demonstrated the function of the sensor array in estimating airspeeds and angle of attacks, achieving average solving errors of 0.15 m/s and 0.37°, respectively.