Application of Deep Learning in Robot Visual Identification

• Main Authors: LIN, YU-SHENG, 林祐生
• Other Authors: YEN, CHIA-MING
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/6qk4dt

碩士 === 國立虎尾科技大學 === 機械設計工程系碩士班 === 107 === This study combines the Deep Learning Architecture Tensorflow with the robotic arm to design a system for identifying items by image recognition. It is divided into two parts: machine learning for image classification and six-axis robotic arm control. This architecture is verified by the mechanical arm classification of bolts and nuts. During the research,the data needs to be marked in advance and input into the neural network for training.Through the observation of the loss rate, it can be known whether the model converges or not. After the training is completed, the neural network is output and stored. Afterwards, enter the image into the system for identification to obtain the coordinates, and use the six-axis robotic arm to perform path planning and classify the obtained coordinate points. This paper is validated by the "intelligent classification" topic of "Hiwin Intelligent Robotic Arm Competition". Applying deep learning combined with the feasibility of images and robots, this method of image recognition is designed: the bolts and nuts scattered in the box, Sort by image recognition. From the actual verification, the method proposed in this paper can effectively achieve the following three objectives: (1) labeling data through the virtual environment; (2) solving the identification of stacked items; (3) making the robot arm accurately pick up the objects and classify.