TY - GEN
T1 - Surface classification for sensor deployment from UAV landings
AU - Anthony, David
AU - Basha, Elizabeth
AU - Ostdiek, Jared
AU - Ore, John Paul
AU - Detweiler, Carrick
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/6/29
Y1 - 2015/6/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84938284064&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938284064&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2015.7139678
DO - 10.1109/ICRA.2015.7139678
M3 - Conference contribution
AN - SCOPUS:84938284064
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3464
EP - 3470
BT - 2015 IEEE International Conference on Robotics and Automation, ICRA 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 IEEE International Conference on Robotics and Automation, ICRA 2015
Y2 - 26 May 2015 through 30 May 2015
ER -