The use of convolutional neural networks for training the manipulation robot to capture objects

Authors: Jamal Mais
Published in issue: #1(42)/2020
DOI: 10.18698/2541-8009-2020-1-571
Category: Mechanical Engineering and Machine Science | Chapter: Robots, Mechatronics, and Robotic Systems
Keywords: robot, manipulator, capture operation, neural network, convolutional neural network, machine learning, neural network training, artificial intelligence
Published: 31.01.2020

The capture of various objects is the main task performed by the robot in the study and manipulation in the environment. Given the significant difficulties associated with programming the necessary capture position for each of the objects, it is proposed to use a convolutional neural network to train the robot to capture various objects, taking into account their spatial position. The network was trained using a sample of 800 images of 20 objects. An experimental study showed that the network provides 53.04% success in capturing new objects that were not in the training set. This suggests that with an increase in the training sample, the robot will be able to successfully capture objects that meet certain conditions and are not in the training sample.

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