TY - GEN
T1 - Autonomous meta-classifier for surface hardness classification from UAV landings
AU - Basha, Elizabeth
AU - Watts-Willis, Tristan
AU - Detweiler, Carrick
N1 - Funding Information:
We are grateful to USDA-NIFA 2017-67021-25924, USDA-NIFA 2013-67021-20947, and NSF CNS (CSR-1217400 and CSR-1217428), which partially supported this work.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/13
Y1 - 2017/12/13
N2 - Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. Motivating our work is a prior project [1] that manually developed a surface classifier using an accelerometer; to verify our system functionality, we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. Overall, our system reduces the time and machine learning expertise needed by the user to develop new time-series classifiers usable by the UAV. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool [2].
AB - Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. Motivating our work is a prior project [1] that manually developed a surface classifier using an accelerometer; to verify our system functionality, we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. Overall, our system reduces the time and machine learning expertise needed by the user to develop new time-series classifiers usable by the UAV. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool [2].
UR - http://www.scopus.com/inward/record.url?scp=85041961892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041961892&partnerID=8YFLogxK
U2 - 10.1109/IROS.2017.8206192
DO - 10.1109/IROS.2017.8206192
M3 - Conference contribution
AN - SCOPUS:85041961892
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3503
EP - 3509
BT - IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Y2 - 24 September 2017 through 28 September 2017
ER -