文章基本信息

标题：Object gripping algorithm for robotic assistance by means of deep learning
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作者：Robinson Jimenez-Moreno ; Astrid Rubiano Fonseca ; Jose Luis Ramirez 等
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2020
卷号：10
期号：6
页码：6292-6299
DOI：10.11591/ijece.v10i6.pp6292-6299
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：This paper exposes the use of recent deep learning techniques in the state of the art, little addressed in robotic applications, where a new algorithm based on Faster R-CNN and CNN regression is exposed. The machine vision systems implemented, tend to require multiple stages to locate an object and allow a robot to take it, increasing the noise in the system and the processing times. The convolutional networks based on regions allow one to solve this problem, it is used for it two convolutional architectures, one for classification and location of three types of objects and one to determine the grip angle for a robotic gripper. Under the establish virtual environment, the grip algorithm works up to 5 frames per second with a 100% object classification, and with the implementation of the Faster R-CNN, it allows obtain 100% accuracy in the classifications of the test database, and over a 97% of average precision locating the generated boxes in each element, gripping successfully the objects.
关键词：Convolutional network;CNN regression;Object gripping;Robotic;Virtual environment