Surface classification for sensor deployment from UAV landings
Surface classification for sensor deployment from UAV landings
复制标题
无人机着陆传感器部署的表面分类
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Carrick Detweiler
中科院分区：
文献类型：
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作者：
David J. Anthony;Elizabeth Basha;J. Ostdiek;J. Ore;Carrick Detweiler
使用无人机（UAV）部署传感器网络有望在远程或难以访问的位置提供一种自主且有用的安装方法。一些传感器，如土壤湿度传感器，必须物理安装在软土壤中，但无人机不能很容易地确定土壤的柔软度与遥感传感器。在本文中，我们使用的数据从机载加速度计测量无人机着陆过程中，以确定地面的柔软度。我们收集和分析了来自8种不同材料的200多个数据集：泡沫，地毯，木材，瓷砖，草，泥土，混凝土和木片。在此分析的基础上，我们研究了加速度计和四种分类算法：LDA，QDA，SVM和二叉决策树的一些功能。决策树表现良好，并且易于在无人机上实现。我们在无人机控制系统中实现了这一点，并进行了实验，以验证无人机可以准确地分类表面的柔软度，准确率为90%。这为我们未来开发能够在软土中安装传感器的无人机奠定了基础。
Using Unmanned Aerial Vehicles (UAVs) to deploy sensor networks promises an autonomous and useful method of installation in remote or hard to access locations. Some sensors, such as soil moisture sensors, must be physically installed in soft soil, yet UAVs cannot easily determine soil softness with remote sensors. In this paper, we use data from an onboard accelerometer measured during UAV landings to determine the softness of the ground. We collect and analyze over 200 data sets gathered from 8 different materials: foam, carpet, wood, tile, grass, dirt, concrete, and woodchips. Based on this analysis, we examine a number of features from the accelerometer and four classification algorithms: LDA, QDA, SVM, and binary decision trees. The decision tree performs well and is simple to implement onboard the UAV. We implement this in our UAV control system and perform experiments to verify that the UAV can accurately classify the softness of the surface with 90% accuracy. This lays the groundwork for our future work on developing a UAV capable of installing sensors in soft soil.