Model Pelacakan Area Perhatian Manusia pada Pekerjaan Perakitan Berbasis Self-Optimizing System

This research aims at investigating the area of interest (AOI) of the human operator when conducting automated robotic assembly task. A technical system, so called control cognitive unit (CCU), is applied into this empirical study to represent human cognition system and based on the prior knowledge, to adapt into condition changing of product structure and material supply. To improve the compatibility between human expectation and the technical system, this empirical study is designed and focused on different model of robot behaviour based on number of human-oriented production rule that applied in the models. Participants are expected to recognize the robot’s work pattern regarding assembly sequences using LEGO brick and carburetor and then to predict the position of the next brick or carburetor part. The result of ANOVA test of assembly task using LEGO brick finds significant differences on the fixation duration for the interaction between the model of robot behaviour and the assembly group as well as between the assembly group and AOI. Assembly with carburetor shows a significant difference for interaction between the model of robot behaviour and AOI. Furthermore, the fixation duration of the participants during the assembly processes using the most-human oriented model lead to the shortest fixation duratio

Model Pelacakan Area Perhatian Manusia pada Pekerjaan Perakitan Berbasis Self-Optimizing System

This research aims at investigating the area of interest (AOI) of the human operator when conducting automated robotic assembly task. A technical system, so called control cognitive unit (CCU), is applied into this empirical study to represent human cognition system and based on the prior knowledge, to adapt into condition changing of product structure and material supply. To improve the compatibility between human expectation and the technical system, this empirical study is designed and focused on different model of robot behaviour based on number of human-oriented production rule that applied in the models. Participants are expected to recognize the robot’s work pattern regarding assembly sequences using LEGO brick and carburetor and then to predict the position of the next brick or carburetor part. The result of ANOVA test of assembly task using LEGO brick finds significant differences on the fixation duration for the interaction between the model of robot behaviour and the assembly group as well as between the assembly group and AOI. Assembly with carburetor shows a significant difference for interaction between the model of robot behaviour and AOI. Furthermore, the fixation duration of the participants during the assembly processes using the most-human oriented model lead to the shortest fixation duratio