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Abstract The block erection using a gantry crane is an important process for the production of the ships in a shipyard. The motion of the block should be controlled accurately under the external forces to prevent collision with the structures and the excessive loads on wire ropes. However, it is difficult to control the block during the lifting because the movement of the block is indirectly controlled with various objects such as trolleys, hooks, equalizers, and wire ropes. Therefore, we proposed the Deep Reinforcement Learning (DRL)-based block lifting method in this study. The DRL-based block lifting method can control the block under the change of the center of gravity and modelling uncertainty. Furthermore, the DRL-based block lifting method can provide robust control with an unexpected motion of the block due to the unexpected external disturbance. The position, orientation and angular velocity of the block and hoisting speed of wire ropes were set as the input state of the neural network of DRL. The hosting speed of wire ropes was controlled as the output action of DRL. The functions to minimize the change of orientation and to stabilize the speed of the block were set as the reward of DRL. In this study, the deep deterministic policy gradient (DDPG) method of DRL, which is a kind of off-policy actor-critic method, was applied to solve the problem with continuous state space and continuous multi-action space. To verify the DRL-based block lifting method proposed in this study, it was compared with traditional control algorithms for various simulation examples. As a result, the proposed method could effectively control the block with the modelling uncertainty. Also, the proposed method could respond to the unexpected motion of the block effectively due to the unexpected external disturbance.
Publication Date 2022-04-28

Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Seung-Ho Ham, "A Block Lifting Method with Wire Ropes Based on Deep Reinforcement Learning", Proceedings of ICDM(International Conference on Decarbonization and Digitalization in Marine Engineering) 2022, Si-Heung, Korea, 2022.04.28-29


List of Articles
번호 분류 제목 Publication Date
66 Domestic Conference 이혜원, 노명일, 박정호, "선박 추적을 위한 센서 데이터 연관 방법", 2022년도 대한조선학회 춘계학술발표회, 제주, pp. 309, 2022.06.02-04 file 2022-06-02
» International Conference Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Seung-Ho Ham, "A Block Lifting Method with Wire Ropes Based on Deep Reinforcement Learning", Proceedings of ICDM 2022, Si-Heung, Korea, 2022.04.28-29 2022-04-28
64 International Conference Dong-Guen Jeong, Myung-Il Roh, Ki-Su Kim, Jun-Sik Lee, Dae-Hyuk Kim, Wang-Seok Jang, "A Method for Route Planning of Small Ships in Coastal Areas", Proceedings of ICDM 2022, Si-Heung, Korea, 2022.04.28-29 2022-04-28
63 Domestic Conference 김진혁, 노명일, 여인창, 김기수, 오민재, "딥 러닝을 이용한 소형 선박의 저항 예측", 2022년도 대한조선학회 춘계학술발표회, 제주, pp. 290, 2022.06.02-04 file 2022-06-02
62 Domestic Conference 박정호, 노명일, 이혜원, 조영민, 손남선, "영상 기반의 선박 추적을 위한 개선된 방법", 2022년도 대한조선학회 춘계학술발표회, 제주, p.311, 2022.06.02-04 file 2022-06-02
61 International Conference Do-Hyun Chun, Myung-Il Roh, In-Chang Yeo, "Optimum Design of Membrane-type LNG Tanks for Installing Insulation", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10 2022-11-07
60 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
59 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
58 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
57 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
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