期刊
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
卷 86, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2023.102664
关键词
Augmented reality; Indoor positioning; Production monitoring system; Human factors; Human -in -the -loop
With the increasing demand for product customization, the capacity of traditional production lines is facing new challenges. The traditional monitoring system is unable to meet the information interaction requirements between the factory management level and the executive level due to information redundancy and complex production process. To address this gap, a novel mobile production monitoring system (AIMPMs) with human-in-the-loop control is proposed, leveraging cutting-edge AR and indoor positioning technique. The proposed system utilizes UWB and IMU fusion indoor positioning technology for accurate indoor positioning information, and includes a lightweight indoor map and a nearest-neighbor decision algorithm for virtual monitoring.
With the increasing demand for product customization, the exponential increase in large-scale and small-batch production orders has yielded new challenges for the capacity of traditional production lines. Due to the information redundancy and complex production process on the production site of the factory, the traditional monitoring system is incapable of meeting the information interaction requirements between the factory management level and the executive level. Meanwhile, the traditional augmented reality (AR) based on image recognition is not suitable in the complex industrial environments. To address the gap, we propose a novel mobile production monitoring system (AIMPMs) with human-in-the-loop control by leveraging the cutting-edge AR and indoor positioning technique. In the proposed system, Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) fusion indoor positioning technology is proposed, which provides accurate indoor positioning information for the production factors in the factory. Subsequently, we build the lightweight indoor map for positioning that can serve as the location reference and path planning, and a nearest-neighbor decision algorithm with double rejection decision (NN-DRD) is proposed to match the positioning features to trigger virtual monitoring information. Finally, the AIMPMs is applied in a hydraulic cylinder factory to verify its enforceability and effectiveness, and the human factors evaluation model and index system are constructed to evaluate two systems, (1) the mobile monitoring system based on positioning information, and (2) the mobile monitoring system based on image recognition, respectively. The experimental results indicate that after the application of AIMPMs, the physiological and mental fatigue of the production personnel is immensely decreased. Therefore, the system realizes a smarter and highly humanized human-machine interaction mode with human-in-the-loop control.
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