Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
A Proposal on Feature Analysis for Digging Operation of Excavator by Machine Learning
Kazushige KoiwaiHiromu ImajiToru YamamotoKoji UedaYoichiro Yamazaki
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JOURNALS OPEN ACCESS

2018 Volume 31 Issue 9 Pages 328-335

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Abstract

In the construction field, the improvement of productivity and the work efficiency are demanded by the introduction of automation and ICT technology for the construction machine. However, it is the fact that the work efficiency and productivity depend on the operator skill of the construction machine in the current construction field. Therefore, the work efficiency will be high in the field with the skilled operator. In this paper, the analysis of feature for the digging operation of an excavator by using a random forest is proposed. A random forest is learned on the basis of skilled work states. The operation difference has been verified by the judgment result of the random forest compared with novice, typical, and professional work states. Moreover, the difference of operations has been considered by state flow models which were made from the judgment result of the random forest.

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© 2018 The Institute of Systems, Control and Information Engineers
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