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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
496 Domestic Conference 전도현, 노명일, 유동훈, "강화 학습 및 속도 장애물 기법을 적용한 선박의 충돌 회피 경로 생성 방법", 2024년도 한국CDE학회 동계학술발표회, 평창, pp. 96, 2024.01.29-02.01 file 2024-01-30
495 Domestic Conference 박동규, 노명일, 하지상, "무인 수상정의 개념 설계를 위한 주요 제원 최적화 방법", 2023년도 한국해군과학기술학회 동계학술발표회, 대전, pp. 110-141, 2023.12.07-08 2023-12-08
494 Invited Seminar Myung-Il Roh, "Core AI Technologies for Autonomous Ships", 2023 Annual Autumn Meeting, JASNAOE (Japan Society of Naval Architects and Ocean Engineers), Nagasaki, Japan, 2023.11.27-28 file 2023-11-27
493 Domestic Conference 김진혁, 노명일, 여인창, "선형 설계를 위한 GNN의 적용 방안 연구", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 450, 2023.11.02-03 file 2023-11-03
492 Domestic Conference 김하연, 노명일, 조영민, 하지상, 손남선, "센서 데이터를 활용한 해상 장애물 추적 방법 및 실해역 적용 사례", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 211, 2023.11.02-03 file 2023-11-03
491 Domestic Conference 전도현, 노명일, 이혜원, 유동훈, "복잡한 해상 상황에서의 강화 학습 기반 선박 충돌 회피 방법", 대한조선학회 추계학술발표회, 울산, pp. 453, 2023.11.02-03 file 2023-11-03
490 Domestic Conference 조영민, 노명일, 하지상, 김하연, 손남선, "무인선을 위한 카메라 및 레이더의 센서 융합 방법", 대한조선학회 추계학술발표회, 울산, pp. 225, 2023.11.02-03 file 2023-11-03
489 Domestic Conference 여인창, 노명일, 공민철, 유동훈, 진은석, "LIDAR를 이용한 선박의 자동 접이안 경로 생성 알고리즘", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 181, 2023.11.02-03 file 2023-11-02
488 Domestic Conference 하지상, 노명일, 공민철, 김미진, 김정연, "선박 유닛 모듈의 배관 배치 방법", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 114, 2023.11.02-03 file 2023-11-02
487 Domestic Conference 박동규, 노명일, 하지상, "최적화 기법을 활용한 무인 수상정의 초기 제원 결정", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 111, 2023.11.02-03 file 2023-11-02
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