Kuantama Endrowednes, Tarca Radu, Dzitac Simona, Dzitac Ioan, Vesselenyi Tiberiu, Tarca Ioan
Department of Electrical Engineering, Pelita Harapan University, Tangerang 15811, Indonesia.
Mechatronics Department, University of Oradea, 1 Universitatii St., Oradea 410087, Romania.
Sensors (Basel). 2019 Sep 6;19(18):3849. doi: 10.3390/s19183849.
This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.
本研究对一种使用四旋翼无人机的空气监测开发系统进行了详细分析。数据收集方法基于气体扩散研究，以确定气体源位置并测定气体浓度水平。由于其灵活性和低成本，四旋翼无人机与空气监测传感器集成，以收集所需数据。分析从传感器在四旋翼无人机上的放置及其与产生的涡流的相关性开始。所用传感器的可靠性和响应时间决定了数据收集过程的持续时间。环境的动态特性使得空气监测技术成为最受关注的问题。模式方法已应用于数据收集过程，其中使用兴趣点或网格点标记区域扫描。实验通过操纵一氧化碳（CO）源进行，数据读取采用两种方式：点源，八个采样点按正方形模式排列；非点源，24个采样点按网格模式排列。四旋翼无人机在悬停状态下收集数据，每个点的采样时间为10秒。方差分析方法（ANOVA）也被用作统计算法来分析气体扩散向量。为了解决风的不确定性，使用双变量高斯核分析来估计气体源区域。结果表明，网格模式测量有助于获得更准确的气体源位置和气体浓度数据。螺旋桨产生的涡旋场用于加速气体颗粒向传感器的聚集。风的动态特性导致气体流动向量不断变化。因此，优选更多的采样点，以提高气体源位置预测的准确性。
