In order to reduce the production cost due to the rising wages the manufacturing industry replaces the traditional manpower with automated production equipment to perform material processing or quality inspection However the feeding among machines is still a challenge The goal of this study is to use a robot arm system to replace the part of loading materials manuually or shaking and flattening the piled semi-finished products by the vibratory feeder resulting in an increase in the flexibility and a decrease in the cost In this thesis a RGB-D sensor robot arm and computer vision algorithms are combined to form a system for classifying detecting and grasping objects in the pile Three methods are applied The first method is the detection and classification by analyzing the color and depth images using traditional computer vision methods Next the original Faster R-CNN is improved to multi-modal inputs which are RGB image and raw depth map The RGB-D Faster R-CNN model has a precise detection result with fused RGB-D feature Finally the RGB-D Faster R-CNN is further modified to 3D RGB-D Faster R-CNN which outputs 3D information of object directly
Date of Award | 2019 |
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Original language | English |
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Supervisor | Shu-Mei Guo (Supervisor) |
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A Pile of Objects Detection Classification for Grasping System Using RGB-D Faster R-CNN
宛臻, 李. (Author). 2019
Student thesis: Doctoral Thesis