Authors
Hiromu Imaji, Kazushige Koiwai, Toru Yamamoto, Koji Ueda, Yoichiro Yamazaki
Corresponding Author
Hiromu Imaji
Available Online 1 December 2017.
DOI
https://doi.org/10.2991/jrnal.2017.4.3.4How to use a DOI?
Keywords
human skill, machine learning, random forest, hydraulic excavator
Abstract
In the construction field, the improvement of the work efficiency is one of important problems. However, the work efficiency using construction equipment depends on their operation skills. Thus, in order to increase the work efficiency, the operation skill is required to be quantitatively evaluated. In this study, the Random Forest, one of machine learning method, is adopted as the quantitatively evaluation for the operation skill of construction equipment. Evaluated target is the operation on an excavation to load onto a truck for a hydraulic excavator. The Random Forest learns to classify some states by the pilot pressure of skilled worker’s operation. States are defined as ‘dig’, ‘lift’, ‘dump’, ‘reposition’, and ‘idle’. The Random Forest with the learning result of skilled worker is applied to other worker’s operation. The human skill quantification is verified based on the ‘idle’ state.

Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).