Human Skill Quantification for Excavator Operation using Random Forest

DOI

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 Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Hiromu Imaji
AU  - Kazushige Koiwai
AU  - Toru Yamamoto
AU  - Koji Ueda
AU  - Yoichiro Yamazaki
PY  - 2017
DA  - 2017/12/01
TI  - Human Skill Quantification for Excavator Operation using Random Forest
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 195
EP  - 200
VL  - 4
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2017.4.3.4
DO  - 10.2991/jrnal.2017.4.3.4
ID  - Imaji2017
ER  -