Skip to content
Extra Form
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


  1. Dong-Hoon Jeong, Myung-Il Roh, Seung-Ho Ham, Woo-Young Choi, Chan-Young Lee, Kyung-Min Seo, Dong-Chul Lee, "Simulation-Based Performances Analysis for Naval Ships at the Initial Design Stage", Proceedings of ISOPE 2016, Rhodes, Greece

    CategoryInternational Conference
    Read More
  2. Dong-Hoon Jeong, Myung-Il Roh, Seung-Ho Ham, Luman Zhao, Hye-Won Lee, Sol Ha, "Physics-based Simulation of Offshore Installation Operations Considering Ocean Environmental Loads and Operating Conditions", Proceedings of ASEM 2015, Korea

    CategoryInternational Conference
    Read More
  3. Dong-Guen Jeong, Myung-Il Roh, Ki-Su Kim, Jun-Sik Lee, Dae-Hyuk Kim, Wang-Seok Jang,"A Route Planning Method for Coastal Navigation of Small Ships", Proceedings of ICCAS 2022, Yokohama, Japan, pp. ???, 2022.09.13-15

    CategoryInternational Conference
    Read More
  4. 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

    CategoryInternational Conference
    Read More
  5. Do-Hyun Chun, Myung-Il Roh, Seung-Ho Ham, Hoon-Kyu Oh, Sang-Ok Lee, "Optimum Layout Design of Wedges of Panel for an LNG Tank Considering Amount of Resin Ropes", Proeedings of ISOPE 2019, Honolulu, Hawaii, pp. 1289-1292, 2019.06.16-21

    CategoryInternational Conference
    Read More
  6. 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

    CategoryInternational Conference
    Read More
  7. Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Seung-Ho Ham, "A Method for Automatic Control of Cranes for Block Lifting in Shipyard", Proceedings of PRADS 2022, Dubrovnik, Croatia, pp. ???, 2022.10.09-13

    CategoryInternational Conference
    Read More
  8. 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 2019, Berlin, Germany, 2019.08.28-30

    CategoryInternational Conference
    Read More
  9. 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

    CategoryInternational Conference
    Read More
  10. Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, "Deep Reinforcement Learning-Based Ship Collision Avoidance Considering Collision Risk", Proceedings of TEAM 2022, Istanbul, Turkey, pp. 268, 2021.12.06-07

    CategoryInternational Conference
    Read More
Board Pagination Prev 1 ... 5 6 7 8 9 10 11 12 13 14 Next
/ 14

Powered by Xpress Engine / Designed by Sketchbook

sketchbook5, 스케치북5

sketchbook5, 스케치북5

나눔글꼴 설치 안내


이 PC에는 나눔글꼴이 설치되어 있지 않습니다.

이 사이트를 나눔글꼴로 보기 위해서는
나눔글꼴을 설치해야 합니다.

설치 취소