Spatial Based Deep Learning Autonomous Wheel Robot Using CNN

  • Eko Wahyu Prasetyo Universitas Merdeka Malang
  • Nambo Hidetaka Kanazawa University
  • Dwi Arman Prasetya
  • Wahyu Dirgantara Universitas Merdeka Malang
  • Hari Fitria Windi Universitas Merdeka Malang

Abstract

The development of technology is growing rapidly; one of the most popular among the scientist is robotics technology. Recently, the robot was created to resemble the function of the human brain. Robots can make decisions without being helped by humans, known as AI (Artificial Intelligent). Now, this technology is being developed so that it can be used in wheeled vehicles, where these vehicles can run without any obstacles. Furthermore, of research, Nvidia introduced an autonomous vehicle named Nvidia Dave-2, which became popular. It showed an accuracy rate of 90%. The CNN (Convolutional Neural Network) method is used in the track recognition process with input in the form of a trajectory that has been taken from several angles. The data is trained using Jupiter's notebook, and then the training results can be used to automate the movement of the robot on the track where the data has been retrieved. The results obtained are then used by the robot to determine the path it will take. Many images that are taken as data, precise the results will be, but the time to train the image data will also be longer. From the data that has been obtained, the highest train loss on the first epoch is 1.829455, and the highest test loss on the third epoch is 30.90127. This indicates better steering control, which means better stability.

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Author Biographies

Nambo Hidetaka, Kanazawa University

Electrical engineering and computer science

Dwi Arman Prasetya

Electrical Engineering

Wahyu Dirgantara, Universitas Merdeka Malang

electrical engineering

Hari Fitria Windi, Universitas Merdeka Malang

electrical engineering

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Published
2020-12-22
How to Cite
PRASETYO, Eko Wahyu et al. Spatial Based Deep Learning Autonomous Wheel Robot Using CNN. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 11, n. 3, p. 167-177, dec. 2020. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/68199>. Date accessed: 27 feb. 2021. doi: https://doi.org/10.24843/LKJITI.2020.v11.i03.p05.