Surface classification for sensor deployment from UAV landings

Abstract

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.

Open source R for applying machine learning to RPAS remote sensing images

“…They can be used for supervised and unsupervised classification. They use algorithms that learn from previous computation, and they were recently applied in investigations regarding cotton crop [8], variable-rate fertilization [9], classification of invasive weed species [10], detecting landing sites [11,12], geological mapping [13], Land Use/Land Cover (LULC) classification [14][15][16][17][18].…”

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems

“…Ore et al use UASs to measure properties of a column of water from the air [Ore and Detweiler, 2018]. Anthony et al designed a UAS that could classify the hardness and type of ground based on the forces measured on the UAS as it landed [Anthony et al, 2015]. Others have created a UAS with spiked legs that penetrated the soil in order to measure seismic activity [Stewart et al, 2016].…”