| Title: | OS4-4 Human Skill Quantification for Excavator Operation using Random Forest |
|---|---|
| Publication: | ICAROB2017 |
| Volume: | 22 |
| Pages: | 437-440 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2017.OS4-4 |
| Author(s): | Hiromu Imaji, Kazushige Koiwai, Toru Yamamoto, Koji Ueda, Yoichiro Yamazaki |
| Publication Date: | January 19, 2017 |
| 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 is depend 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 (RF), 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 RF learns to classify some states by the pilot of skilled worker's operation. States are defined as ‘dig', ‘lift', ‘dump', ‘reposition', and ‘idle'. The RF with the learning result of skilled worker is applied to other operator's operation. It is revealed that the ratio of ‘idle' is related to their skill. |
| PDF File: | https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS4/OS4-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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