Affiliation:
1. Department of Mechanical System Design Engineering, Seoul National University of Science and Technology, Seoul, Korea
Abstract
In this study, a system for automatically picking mechanical parts required in the industrial automation field was proposed. In particular, using deep learning, bolts and nuts were recognized and geometric information of these parts was extracted. By applying YOLOv3 specialized in high recognition rate and fast processing speed, the recognition of target object, location, and postural information were obtained. The geometric information for the bolt can be obtained by creating two bounding boxes and calculating the orientation vector formed by these center values of two bounding boxes after successfully detecting two individual bounding boxes. Moreover, to obtain more precise geometric information on bolts and nuts, image distortion compensation on the detected object was done after detecting the center value of the bolt and nut through YOLOv3. Based on this result, it was proven that an automatic picking of the mechanical parts using a five-axis robot was successfully implemented.
Funder
Korea Institute for Advancement of Technology
Subject
Artificial Intelligence,Computer Science Applications,Software
Cited by
4 articles.
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1. Detection of Small Screws Using Machine Learning;2023 International Conference on Information and Communication Technology for Development for Africa (ICT4DA);2023-10-26
2. Autonomous Robotic Bin Picking Platform Generated From Human Demonstration and YOLOv5;Journal of Manufacturing Science and Engineering;2023-08-28
3. Extension of Inverse Kinematic Solution for a Robot to Cope With Joint Angle Constraints;International Journal of Control, Automation and Systems;2023-05-06
4. Drug quality re-inspection based on YOLO deep learning;International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022);2022-10-13