Object Classification and Pick Angle Estimation Using Deep Convolutional Neural Networks for Robot Arm Operation

• Main Authors: ChaoLin, 林超
• Other Authors: Jenn-Jier James Lien
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/knf6mv

碩士 === 國立成功大學 === 資訊工程學系 === 106 === In the field of robots, the operation of objects is a classic problem. Correct manipulation of objects requires judging the position of objects, the angle at which objects are clipped, and the type of objects they belong to. Traditional computer vision judgment algorithms have some problems, such as poor generality, low robustness and high computational cost, which can not meet the needs of complex object operation. In order to solve the above problems, this paper proposes a general object retrieval framework based on deep convolution neural network, which includes three parts: automatic data collection, deep convolution neural network training, and deep convolution neural network testing. It can accomplish the task of object operation more efficiently. Among them, the collection time of single object data is about 2.5 hours, and the training time of neural network is about 6 hours. The total classification accuracy and pinching accuracy of the trained neural network model are 100% and 94.8% respectively, and can be further improved with the increase of the number of data automatically collected. In addition, in the test of neural network, this paper proposes cascaded network architecture to accelerate the operation, which can accelerate the effective operation time from 1.53 seconds of a single network to 0.65 seconds without affecting the pinch and classification accuracy.