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: Kijdech D.; Vongbunyong S.

Publisher: Springer

Publication year: 2024

Volume number: 110

Issue number: 3

ISSN: 0921-0296

eISSN: 1573-0409

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198021242&doi=10.1007%2fs10846-024-02132-0&partnerID=40&md5=f7180aa657b9df1508a928e7ea730b13

Languages: English-Great Britain (EN-GB)

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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 network model 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. © The Author(s) 2024.

Keywords

Mask R-CNN