구글 티처블 머신을 활용한 군사장애물 분류 모델 설계 및 실증 연구

With the recent development of Obstacle Clearance Tank (K-600) that can overcome minefield, rockfall and road crator, ROK Army can shorten the time required to overcome obstacles and increase operation efficiency. However, in order to overcome the lack of military service resources in the future and...

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Published in한국CDE학회 논문집 Vol. 27; no. 2; pp. 137 - 147
Main Authors 황재민(Jaemin Hwang), 마정목(Jungmok Ma)
Format Journal Article
LanguageKorean
Published (사)한국CDE학회 01.06.2022
한국CDE학회
Subjects
기계공학
Deep learning
Obstacle
Modeling and simulation
Teachable machine
Obstacle clearance tank
Online AccessGet full text
ISSN2508-4003
2508-402X
DOI10.7315/CDE.2022.137

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Summary:With the recent development of Obstacle Clearance Tank (K-600) that can overcome minefield, rockfall and road crator, ROK Army can shorten the time required to overcome obstacles and increase operation efficiency. However, in order to overcome the lack of military service resources in the future and be guaranteed to survive operator, Unmanned Obstacle Clearance Tank should be introduced along with artificial intelligence technologies. In order to develop the Unmanned Obstacle Clearance Tank, the initial recognition stage is critical among “recognition- control-action” stages. This study aims to build the obstacle recognition and classification model based on Google teachable machine and verify the model using the real RC-car camera test environment. KCI Citation Count: 2
ISSN:2508-4003
2508-402X
DOI:10.7315/CDE.2022.137