Feature Extraction for Excavator Operation Skill Using CMAC

Abstract

[abstFig src='/00280005/14.jpg' width='300' text='Feature extraction for excavator operation' ] In recent years, technology that includes informatization and automation has been introduced in the construction field. On the other hand, those field still require human operation technology based on experience and skills because various environmental conditions vary from hour to hour. Seasoned technicians have made such operation technology effective at various sites and established skillful techniques. However, the decreasing number and aging of skilled technicians are a social issue, making the skill tradition and development of younger technicians difficult at operation sites that require skillful techniques. This study assumed that the operation of machines by an operator was synonymous with the control of systems by a controller; human operation techniques were considered from the viewpoint of control engineering by regarding an operator as a controller. The control system used to represent the operator consisted of a proportional-integral-derivative (PID) controller and a cerebellar model articulation controller (CMAC) that adjusted the PID gains. A CMAC which is a type of neural network learns human skills as variations in the PID gains and expresses them based on the variations. This study applies the proposed method to a hydraulic excavator swing operation to evaluate skills. Moreover, the difference in the operation skills for the excavator is clarified by obtaining operation data for skilled and younger technicians and examining the variation tendency of PID gains.

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.…”

“…The operation skill is calculated from the preset number of q-neighbor data according to the distance shown in (9). Considering that the skill level can be expressed by the numerical value of the feature vector, the skill can be quantitatively determined by the linearly weighted average, as shown in the following equation:…”

“…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].…”

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

“…Moreover, the skill evaluation by using the skill-based PID controller have been performed. 4,5 These research focus on the change of PID gains to extract the feature of the operation skill for the excavator. However, the evaluated operation is applied to the simple lever operation such as the swing operation.…”

Human Skill Quantification for Excavator Operation using Random Forest

“…Therefore, it is necessary to establish a method to evaluate the operation skills so that it is possible to improve the productivity in the field of construction industry. In previous studies, the human skill evaluation based on the control engineering approch 3 has been proposed and the evaluation of operation skills based on interviews has been investigated. 4 However, these methods focus on the qualitative evaluation only and it is difficult to utilize them to improve the productivity.…”