@inbook{d16f48b28b8e42e4be136fe8eb4765ec,
title = "Online Soil Classification Using a UAS Sensor Emplacement System",
abstract = "Deployment of sensors in hard-to-access locations can improve data gathering for scientific studies. We have developed a sensor emplacement system that can be mounted to unmanned aircraft systems with vertical takeoff and landing capabilities to autonomously auger a sensor into the ground. Various techniques can be chosen to enhance the augering process when certain characteristics of the soil are known. Moisture content and compressive strength are the soil characteristics that most impact the augering process, yet directly measuring them would require additional sensors to an already-burdened airframe. We address this through a novel means of predicting these soil characteristics within the first 30 s of an average 85 s augering evolution using onboard sensors and a Gaussian process regression scheme that predicts the soil moisture content and compressive strength with accuracy of 86.53% and 90.53% of the respective measured values.",
keywords = "Field robotics, Machine learning, Soil classification",
author = "Adam Plowcha and Jacob Hogberg and Carrick Detweiler and Justin Bradley",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2021",
doi = "10.1007/978-3-030-71151-1_16",
language = "English (US)",
series = "Springer Proceedings in Advanced Robotics",
publisher = "Springer Science and Business Media B.V.",
pages = "174--184",
booktitle = "Springer Proceedings in Advanced Robotics",
}