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Abstract The block erection using a crane such as a gantry crane and a floating crane is one of the most important processes in the production of ships and offshore structures. To mount the block with the correct position and angle, it is important to control the block accurately not to occur the unexpected movement like rotation. However, it is difficult to apply the existing control theory to the block lifting operation because the movement of the block is controlled indirectly with the control of various objects such as the crane and wire ropes. To solve this problem, a block control method based on deep reinforcement learning is proposed in this study. The proposed method is easier to control the block with wire ropes and to consider irregular external force than existing control theory. In this study, the angle and angular velocity of the lifting block and the hoisting speed of each wire rope that can affect the motion of the block are set as states of reinforcement learning, and the hoisting speed that is the control object is set as an action of reinforcement learning. The reward function of reinforcement learning is designed to increase when the angle of the block decrease and the speed of the block is close to the target speed. In this study the policy gradient method which is a kind of policy-based methods of deep reinforcement learning is used to solve the problem with continuous states and action. To check the applicability and feasibility of the proposed method, The block lifting simulation is performed using the existing control theory and the proposed method. We compared the proposed method with the existing control theory. The result shows that the proposed method can minimize the motion of the lifting block more effectively than the existing control theory.
Publication Date 2019-08-28

Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Seung-Ho Ham, "A Crane Movement Control for Stability of Block Erection Based on Deep Reinforcement Learning", MIM(International Federation of Automatic Control) 2019, Berlin, Germany, 2019.08.28-30


List of Articles
번호 분류 제목 Publication Date
456 International Conference Myung-Il Roh, "Physics-based Simulation for Design, Production, and Installation of Ships and Offshore Structures", International Symposium on Computational Design and Engineering, Ho Chi Minh, Vietnam, 2017.12.13-16 2017-12-15
455 International Conference Myung-Il Roh, "Simulation Based Engineering for Ship and Offshore Plant", International Ocean Technology Conference & Expo (IOTCE 2015), Qingdao, China, 2015.09.01-03 file 2015-09-02
454 International Conference Ki-Su Kim, Myung-Il Roh, Seung-Ho Ham, Sol Ha, "Evacuation Analysis of Passenger Ships Considering Intermediate Flooding", Proceedings of International Symposium on PRADS 2022, Dubrovnik, Croatia, pp. 628-632, 2022.10.09-13 file 2022-10-10
453 Domestic Conference 전도현, 노명일, 이혜원, 조영민, 진은석, 유동훈 "선박 정보의 불확실성을 고려한 확률론적 충돌 위험도 산정", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 94, 2021.08.25-28 file 2021-08-26
452 Domestic Conference 송하민, 노명일, 하지상, 김기수, "해군 함정의 승조원 구성 시뮬레이션 방법", 2022년도 대한조선학회 추계학술발표회, 창원, 2022.11.09-11 2022-11-10
451 Domestic Conference 공민철, 노명일, 이혜원, 전도현, 조영민, 박정호, "자율 운항 기술 검증을 위한 VR 기반 시뮬레이션 프로그램", 2023년도 한국CDE학회 동계학술발표회, 평창, pp. 48, 2023.02.08-11 file 2023-02-09
450 Domestic Conference 박정호, 노명일, 이혜원, 조영민, 손남선, "딥 러닝 기반 다중 카메라 영상을 이용한 해상 장애물 탐지 추적에 관한 연구", 2022년도 한국항해항만학회 추계학술발표회, 부산, pp. 186, 2022.11.10 file 2022-11-10
449 Domestic Conference 정동근, 노명일, 여인창, 공민철, 김기수, 이준식, 유원철, "다양한 해상 객체를 반영한 소형 선박의 항로 계획법", 2022년도 대한조선학회 추계학술발표회, 창원, 2022.11.09-11 2022-11-11
448 International Conference Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, "Estimation of the Hydrodynamic Performance of the Parameterized Hull Forms Using Deep Learning", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10 2022-11-06
447 International Conference Yeongmin Jo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Ship Tracking Method under Dynamic Characteristic Changes with LSTM", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10 2022-11-06
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