Surface classification for sensor deployment from UAV landings

David Anthony, Elizabeth Basha, Jared Ostdiek, John Paul Ore, Carrick Detweiler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Robotics and Automation, ICRA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3464-3470
Number of pages7
EditionJune
ISBN (Electronic)9781479969234
DOIs
StatePublished - Jun 29 2015
Event2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States
Duration: May 26 2015May 30 2015

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
NumberJune
Volume2015-June
ISSN (Print)1050-4729

Other

Other2015 IEEE International Conference on Robotics and Automation, ICRA 2015
Country/TerritoryUnited States
CitySeattle
Period5/26/155/30/15

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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