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基于四旋翼无人机的气体监测终端设计

Design of Gas Monitoring Terminal Based on Quadrotor UAV.

作者信息

Liu Yang, Chen Lei, Fan Shurui, Zhang Yan

机构信息

Tianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.

出版信息

Sensors (Basel). 2022 Jul 18;22(14):5350. doi: 10.3390/s22145350.

Abstract

The problem of air pollution is an increasingly serious worldwide. Therefore, in order to better monitor the gas components in the atmosphere, the design of a gas monitoring terminal based on a quadrotor UAV, including software and hardware design, is hereby carried out. Besides, a pump-suction series cavity is designed to reduce the influence of airflow disturbance on the UAV, which is verified to possess a certain anti-interference ability through Computational Fluid Dynamics(CFD) simulation experiments. In addition, a linear regression algorithm is used for sensor calibration and a polynomial piecewise regression method is used for temperature compensation. The experimental results show that the R of the model reaches 0.9981, the fitting degree is rather high, and the output is closer to the real gas concentration value after calibration. At the same time, the temperature compensation parameters are determined, which considerably improves the accuracy of the entire hardware terminal. Finally, the vehicle exhaust monitoring experiment is conducted, and the experimental results show that this scheme can successfully detect the exhaust position of the vehicle exhaust under the interference of the downwash flow of the UAV, thereby proving the reliability and accuracy of the monitoring terminal.

摘要

空气污染问题在全球范围内日益严重。因此,为了更好地监测大气中的气体成分,特此开展基于四旋翼无人机的气体监测终端设计,包括软件和硬件设计。此外,设计了一种泵吸式串联腔体以减少气流扰动对无人机的影响,通过计算流体动力学(CFD)模拟实验验证其具有一定的抗干扰能力。另外,采用线性回归算法进行传感器校准,采用多项式分段回归方法进行温度补偿。实验结果表明,模型的R达到0.9981,拟合度相当高,校准后输出更接近真实气体浓度值。同时,确定了温度补偿参数,大幅提高了整个硬件终端的精度。最后进行了汽车尾气监测实验,实验结果表明该方案能够在无人机下洗流干扰下成功检测到汽车尾气的排放位置,从而证明了监测终端的可靠性和准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b8e7/9320789/a1bd0d2a14d3/sensors-22-05350-g001.jpg

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