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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
363 Domestic Conference 이원재, 노명일, 이성준, 하지상, 석영수, 오민재, "선박 운항 시 주변 인지를 위한 물체 탐지, 추적 및 거리 추정 방법", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 431, 2019.10.24-26 file 2019-10-24
362 Domestic Conference 하지상, 노명일, 이혜원, 은종호, 박종진, "실시간 AIS 데이터를 이용한 해상 충돌 회피 알고리즘 연구", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 430, 2019.10.24-26 file 2019-10-25
361 International Conference Luman Zhao, Myung-Il Roh, Hye-Won Lee, Do-Hyun Chun, Sung-Jun Lee, "A Collision Avoidance Method of Multi-ships Based on Deep Reinforcement Learning Considering COLREGs," Proceedings of ICCAS 2019, Rotterdam, Netherlands, pp. 85-88, 2019.09.24-26 file 2019-09-24
360 Domestic Conference 노명일, "스마트 함정의 구현을 위한 설계 기술 연구", 함정기술연구회 하계연구발표회, 진해, 2019.07.18-19 2019-07-18
359 Domestic Conference 오민재, 노명일, 김범수, 김용환, "선박의 운항 효율을 고려한 선형 변환 방법", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 480, 2019.10.24-2019.10.25 file 2019-10-24
» International Conference 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 file 2019-08-28
357 International Conference 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 file 2019-06-19
356 International Conference Min-Jae Oh, Myung-Il Roh, Young-Soo Seok, and Sung-Jun Lee, "Optimization of a Ship Hull Form using Deep Learning", Proceedings of ACDDE 2019, Penang, Malaysia, 2019.07.07-10 file 2019-07-08
355 International Conference Sung-Jun Lee, Myung-Il Roh, Min-Jae Oh, Youngsoo Seok, Won-Jae Lee, June-Beom Lee, Hyun Soo Kim, "Image-based Object Detection and Tracking Method for Ship Navigation," Proceedings of ICCAS 2019, Rotterdam, Netherlands, pp. 89-92, 2019.09.24-26 file 2019-09-24
354 Conference Chairman Session 7 (Ship & Ocean Engineering), 2019년도 한국CDE학회 학술발표회, 횡성, 2019.01.23-26 2019-01-24
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