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Abstract Ports often experience congestion due to the presence of other ships. This congestion poses challenges for ship berth-ing, and even minor errors during the berthing process can lead to significant accidents. Therefore, it is crucial to accu-rately detect the port surroundings and generate a route for automatic berthing based on this accurate detection. To ad-dress these concerns, this study proposes a method that utilizes LIght Detection And Ranging (LIDAR) to detect nearby objects, such as other ships and quays, and generate automatic berthing routes. LIDAR is known for its high detection accuracy compared to other sensors. However, LIDAR data is typically represented as a point cloud, requir-ing the de-tection of individual objects within it. This study employed a deep learning-based method for precise object detection in the point cloud.
Deep learning-based object detection methods have gained significant attention recently. However, they often re-quire large amounts of data for effective training. While various sensor data related to autonomous vehicle navigation are openly available, similar resources for ship navigation are limited. To overcome this limitation, this study collected virtual data by implementing a virtual marine environment using Unity and LIDAR sensors. Object detection using LIDAR alone is insufficient for generating automatic berthing routes. Therefore, this study incorporated real-time ship location recogni-tion and the generation of a surrounding map. The generated map enables the recognition of the ship's surrounding situa-tion, facilitating the creation of automatic berthing routes. The proposed method was applied and validated in a virtual marine environment using Unity, confirming the high accuracy of object detection. Additionally, the effectiveness of generating automatic berthing routes was also demonstrated.
Publication Date 2023-10-19

In-Chang Yeo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Method for Automatic Berthing of a Ship Using LIDAR", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20


List of Articles
번호 분류 제목 Publication Date
512 International Conference Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, "A Method for the Automatic Generation of Hull Form Surfaces Based on MLP (Multi-Layer Perceptron) Considering Design Requirements", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09 file 2024-11-05
511 Domestic Conference 여인창, 노명일, 공민철, 유동훈, 진은석, "LIDAR를 이용한 선박 자동 접이안 방법", 2024년도 대한조선학회 춘계학술발표회, 제주, p. 412, 2024.05.23-25 file 2024-05-24
510 Domestic Conference 김진혁, 노명일, 여인창, "다층 퍼셉트론 (MLP)을 이용한 선형 격자 구조의 자동 변환 방법", 2024년도 대한조선학회 춘계학술발표회, 제주, p. 600, 2024.05.23-25 file 2024-05-24
509 Domestic Conference 박동규, 노명일, 공민철, 전도현, "복원성 및 조종성을 고려한 무인 수상정의 초기 제원 결정 방법", 2024년도 대한조선학회 춘계학술발표회, 제주, p. 199, 2024.05.23-25 file 2024-05-23
508 Domestic Conference 박동규, 노명일, 공민철, 전도현, "비손상 복원성을 고려한 개념 설계 단계 무인 수상정의 주요 제원 최적화 방법", 2024년도 함정기술무기체계 세미나, 진해, pp. 277, 2024.04.25-26 file 2024-04-26
507 International Conference In-Chang Yeo, Myung-Il Roh, Min-Chul Kong, Dongki Min, Dongguen Jeong, "An Automated Method for the Review of a Ship’s Safety Plan Based on Deep Learning", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21 file 2024-06-17
506 Domestic Conference 전도현, 노명일, 이혜원, "데이터 불확실성 기반 충돌 위험도 평가 및 강화 학습 기반 충돌 회피", 2024년도 스마트전기선박연구회 동계학술발표회, 부산, 2024.02.15-16 file 2024-02-16
505 International Conference Min-Chul Kong, Myung-Il Roh, In-Chang Yeo, In-Su Han, Dongki Min, Dongguen Jeong, "Methods for Graph Conversion and Pattern Recognition for P&IDs", Proceedings of IMDC 2024, Amsterdam, Netherland, 2024.06.02-06 file 2024-06-05
504 International Conference Jisang Ha, Myung-Il Roh, Min-Chul Kong, Mijin Kim, Jeoungyoun Kim, Nam-Kug Ku, "An Automated Method for Pipe Routing in Ship Unit Modules", Proceedings of IMDC 2024, Amsterdam, Netherland, 2024.06.02-06 file 2024-06-05
» International Conference In-Chang Yeo, Myung-Il Roh, Hye-Won Lee, Donghun Yu, "A Method for Automatic Berthing of a Ship Using LIDAR", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20 file 2023-10-19
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