Feature Extraction for Digging Operation of Excavator Based on Data-Driven Skill-Based PID Controller

Abstract

Improvement of the work efficiency is demanded by aging and reducing of the working population in the construction field, so that some automation technologies are applied to construction equipment, such as bulldozers and excavators. However, not only the automation technologies but also expert skills are necessary to improve the work efficiency. In this paper, the human skill evaluation is proposed by the data-driven skill-based PID controller. The proposed method is applied to the excavator digging operation. As the result, the difference between the novice operation and the skilled operation is extracted. Moreover, the numerical difference is clarified based on the result.

Construction of a Skill Evaluation System Based on database‐driven Approach for a Hydraulic Excavator

“…The swing operation occupies 60% or more of one cycle in general excavation work; therefore, the skill of the swing operation is significantly related to productivity of excavation work. As in the previous study [9,10], the differences in operation skills appear in the deceleration. As the experimental condition to verify the proposed method, an operator must input the lever fully during acceleration.…”

“…In a previous study, differences in the operational characteristics of a hydraulic excavator operator were quantitatively extracted; however, there was no mention of a certain criterion for comparing operational features [9,10]. In other industries, research is being conducted for the quantification of maneuvering skills of motorcycle riders [11].…”