Abstract | For safe operation, a navigator needs to accurately recognize the circumstance around the ship and constantly focus on objects that require attention. Therefore, the navigator should properly utilize the information of the field of vision seen from the ship and the sensor data such as AIS (Automatic Identification System), RADAR (RAdio Detection And Ranging), etc. However, since such data have different dimensions and meanings, it is not easy to interpret except for professionals, and it is not intuitive. In addition, it is difficult to obtain consistent and high-accuracy data depending on the characteristics of each sensor (detection distance, detection period, error, blind spot, etc.) and whether other ships are equipped with the AIS system. Therefore, we developed an integrated navigation assistance system that can effectively report sensor data to the navigator by sensor data fusion. We tracked the positions of ships by sensor data fusion through the tracking filter. To supplement the limitations of sensors, we used the object detection model. Collision risk was quantitatively calculated using the detected ships and tracking data, and the collision-free path of the own ship was generated. The above processes were modularized and integrated, and communication packets were defined to enable data exchange between each module. Finally, we implement a navigation assistance system that efficiently provides necessary information for navigating using AR (Augmented Reality) technology. In addition to ship detection and tracking data, the ship needs to visualize its own sensor data, such as rudder angle and engine RPM. Various data was provided as an intuitive UI by appropriately overlapping the information within the image. The integrated navigation assistance system was mounted on a test ship in actual operation, and its effectiveness was confirmed by transmitting necessary information to the land command center. |
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Publication Date | 2022-11-09 |
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한인수, 노명일, 공민철, 이정렬, 박서윤, "자연어 처리 기술을 활용한 선박 규정 검색 알고리즘", 대한조선학회 추계학술발표회, 울산, pp. 113, 2023.11.02-03
-
김정연, 노명일, 이현승, 이인석, 김미진, 신동규, 유한준, "도면 내 배관 시스템의 자동 매칭 알고리즘", 2023년도 대한조선학회 추계학술발표회, 울산, 2023.11.02
-
공민철, 노명일, 하지상, 김미진, 김정연, "GNN 기반 P&ID의 패턴 인식 및 분석 방법", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 108, 2023.11.02-03
-
Yeong-min Jo, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Do-Hyun Chun, Min-chul Kong, "An Improved Method for the Sensor Fusion for Autonomous Ships", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20
-
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
-
Min-Chul Kong, Myung-Il Roh, Jisang Ha, Mijin Kim, Jeoungyoun Kim, "A Method for the Generation of Optimal Patterns for Equipment Unit Modules in the Engine Room", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20
-
여인창, 노명일, 공민철, 민동기, 정동근, "선박의 Safety Plan 검토를 위한 자동 데이터 생성과 딥 러닝 기반 객체 검출", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 13, 2023.08.23-26
-
한인수, 노명일, 공민철, "딥 러닝을 활용한 P&ID 내 장비 인식 방법", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 11, 2023.08.23-26
-
공민철, 노명일, 여인창, 민동기, 정동근, "그래프를 활용한 P&ID 내 장비의 연결 관계 표현 및 분석", 2023년도 한국CDE학회 하계학술발표회, 제주, p. 53, 2023.08.23-26
-
김하연, 노명일, 하지상, 조영민, 이혜원, "센서 데이터를 활용한 딥 러닝 기반 해상 장애물의 추적 방법", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 12, 2023.08.23-26