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최병희(B. H. Choi),조예찬(Y. C. Jo),박정현(J. H. Park),최성운(S. W. Choi),조상훈(S. H. Cho),김바다(B. D. Kim),박기협(K. H. Park),김주호(J. H. Kim),이대엽(D. Lee) 유공압건설기계학회 2023 유공압건설기계학회 학술대회논문집 Vol.2023 No.5
In many equipment, including construction machinery that operates with hydraulic oil, it is very important to solve the problem of the generation of fine particles in the hydraulic circuit and the inability to use the equipment due to causes such as damage of parts and repair. In this study, as a base study to solve these problems, a study that can detect the color change of hydraulic oil due to particles mixed in hydraulic oil in real time using machine learning was conducted. Using the k-NN classification algorithm, the color change of hydraulic oil is classified into ten classes so that the color can be recognized in real time. It is planned to carry out the development of the sensor system in the following study.
이재혁(J. H. Lee),조예찬(Y. C. Jo),장재원(J. W. Jang),고경주(K. J. Ko),박정현(J. H. Park),최병희(B. H. Choi),최성운(S. W. Choi),조상훈(S. H. Cho),이대엽(D. Lee) 유공압건설기계학회 2023 유공압건설기계학회 학술대회논문집 Vol.2023 No.5
The use for driving robots is increasing in tests of electric construction machinery and automotive using chassis dynamometer that require long hours of operation in order for battery SOC tests. Existing driving robots are not very convenient, such as taking a lot of time to install. Therefore, in this study, basic research was conducted for the development of a driving robot that reduced the installation time and improved the followability of the reference speed profile by using deep learning. In order to follow the speed profile of the standard test cycle according to the regulations, a base study that can control the throttle by using the visual information in real time was conducted using a rig device and deep learning. Based on the results of this study, it is planned to conduct follow-up studies using test vehicle and chassis dynamometer in the next stage.
박정현(J. H. Park),최병희(B. H. Choi),최성운(S. W. Choi),조상훈(S. H. Cho),이재혁(J. H. Lee),이주연(J. Y. Lee),인수환(S. H. Ihn),이대엽(D. Lee) 유공압건설기계학회 2023 유공압건설기계학회 학술대회논문집 Vol.2023 No.5
As the logistics industry continues to grow, the use of unmanned equipment such as unmanned pallet trucks is increasing. In this study, a path control study was conducted using a three-wheeled pallet truck rig with driving power of 1,500 W and steering power of 400 W using MPC (Model Prediction Control) algorithm for path control of a three wheel unmanned pallet truck. Through the analysis, the main control variables of the driving and steering motors were created based on the main path coordinates, and using this, it was confirmed that the driving of the element motion required in the pallet work was possible by following the main coordinates given through the driving experiment. In the future, based on this study, we plan to conduct research that adds dynamics modeling and further enhanced control.
삼륜 키네마틱스를 이용한 팔레트 트럭의 모델 예측 경로 제어 연구
박정현(H. Park),손광희(K. H. Shon),김현수(H. S. Kim),최성운(S. W. Choi),이대엽(D. Lee) 유공압건설기계학회 2022 유공압건설기계학회 학술대회논문집 Vol.2022 No.12
Due to a growth of the logistics industry, a decrease in skilled manpower, and an increase in labor costs, the importance of unmanned automation logistics equipment is gradually increasing. In this study, for the purpose of realizing a manual pallet truck as unmanned driving, a predictive path control system using a model using three-wheeled nonholonomic kinematics consisting of one wheel, acting simultaneously as a driving and steering wheel, and two auxiliary wheels was designed and constructed. A rig system acting as a platform to simulate and develop algorithm for the full size equipment was controlled using an embedded controller and driving and steering servo motors. Throughout experiments using the rig as a platform, it was confirmed that model predictive path control was possible along given goal and object coordinates.