Online Soil Classification Using a UAS Sensor Emplacement System

Adam Plowcha, Jacob Hogberg, Carrick Detweiler, Justin Bradley

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Advanced Robotics
PublisherSpringer Science and Business Media B.V.
Pages174-184
Number of pages11
DOIs
StatePublished - 2021

Publication series

NameSpringer Proceedings in Advanced Robotics
Volume19
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

Keywords

  • Field robotics
  • Machine learning
  • Soil classification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Engineering (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Applied Mathematics

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