DOI: https://doi.org/10.14710/jtsiskom.2022.14474

An Embedded Computer Vision using Convolutional Neural Network for Maze Classification and Robot Navigation

Satya Wacana Christian University, Indonesia

Received: 12 May 2022; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Gunawan Dewantoro, Dinar Rahmat Hadiyanto, Andreas Ardian Febrianto
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Traditionally, the maze solving robots employ ultrasonic sensors to detect the maze walls around the robot. However, this approach only perceives the presence of the objects without recognizing the type of these objects. Therefore, computer vision has become more popular for classification purpose in robot applications. In this study, a maze solving robot is equipped with a camera to recognize the types of obstacles in a maze. The types of obstacles are classified as: intersection, dead end, T junction, finish zone, start zone, straight path, T–junction, left turn, and right turn. Convolutional neural network is employed to train the robot using a total of 24,000 images to recognize the obstacles. Jetson Nano development kit is used to implement the trained model and navigate the robot. The results shows an average training accuracy of 82% with a training time of 30 minutes 15 seconds. As for the testing, the lowest accuracy is 90% for the T-junction with the computational time being 500 milliseconds for each frame. Therefore, the convolutional neural network is adequate to serve as classifier and navigate a maze solving robot.
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Keywords: convolutional neural network; maze; navigation; classification; robot

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Section: Original Research Articles
Language : EN
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