Manipulation of a Complex Object Using Dual‑Arm Robot with Mask R‑CNN and Grasping Strategy
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Dumrongsak Kijdech and Supachai Vongbunyong
Publisher: Springer
Publication year: 2024
Journal acronym: JIRS
Volume number: 110
Start page: 1
End page: 16
Number of pages: 16
ISSN: 0921-0296
eISSN: 1573-0409
URL: https://link.springer.com/article/10.1007/s10846-024-02132-0
Languages: English-United States (EN-US)
Abstract
Hot forging is one of the common manufacturing processes for producing brass workpieces. However forging produces flash which is a thin metal part around the desired part formed with an excessive material. Using robots with vision system to manipulate this workpiece has encountered several challenging issues, e.g. the uncertain shape of flash, color, reflection of brass surface, different lighting condition, and the uncertainty surrounding the position and orientation of the workpiece. In this research, Mask region-based convolutional neural network together with image processing is used to resolve these issues. The depth camera can provide images for visual detection. Machine learning Mask region-based convolutional neural networkmodel was trained with color images and the position of the object is determined by the depth image. A dual arm 7 degree of freedom collaborative robot with proposed grasping strategy is used to grasp the workpiece that can be in inappropriate position and pose. Eventually, experiments were conducted to assess the visual detection process and the grasp planning of the robot.
Keywords
artificial intelligent, Convolutional neural networks (CNN), Robotics