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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.
Publication Date 2022-11-09
Min-Chul Kong, Myung-Il Roh, Jisang Ha, Jeong-Ho Park, EunSeok Jin, Donghun Yu, "Integrated Navigation Assistance System Using Augmented Reality", Proceedings of G-NAOE 2022, Changwon, Korea, 2022.11.06-10

List of Articles
번호 분류 제목 Publication Date
458 International Conference Myung-Il Roh, "Applications of Deep Learning in Ship Design, Production, and Operation Stages", Proceedings of ICDM(International Conference on Decarbonization and Digitalization in Marine Engineering) 2022, Siheung, Korea, 2022.04.28-29 2022-04-29
457 Domestic Conference 노명일, "선박 설계, 생산 및 운용 단계에서의 딥 러닝 활용 예, 2020년도 선박해양플랜트구조연구회 워크샵, 2021.02.18 2021-02-18
456 International Conference Myung-Il Roh, "Physics-based Simulation for Design, Production, and Installation of Ships and Offshore Structures", International Symposium on Computational Design and Engineering, Ho Chi Minh, Vietnam, 2017.12.13-16 2017-12-15
455 International Conference Myung-Il Roh, "Simulation Based Engineering for Ship and Offshore Plant", International Ocean Technology Conference & Expo (IOTCE 2015), Qingdao, China, 2015.09.01-03 file 2015-09-02
454 International Conference Ki-Su Kim, Myung-Il Roh, Seung-Ho Ham, Sol Ha, "Evacuation Analysis of Passenger Ships Considering Intermediate Flooding", Proceedings of International Symposium on PRADS 2022, Dubrovnik, Croatia, pp. 628-632, 2022.10.09-13 file 2022-10-10
453 Domestic Conference 전도현, 노명일, 이혜원, 조영민, 진은석, 유동훈 "선박 정보의 불확실성을 고려한 확률론적 충돌 위험도 산정", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 94, 2021.08.25-28 file 2021-08-26
452 Domestic Conference 송하민, 노명일, 하지상, 김기수, "해군 함정의 승조원 구성 시뮬레이션 방법", 2022년도 대한조선학회 추계학술발표회, 창원, 2022.11.09-11 2022-11-10
451 Domestic Conference 공민철, 노명일, 이혜원, 전도현, 조영민, 박정호, "자율 운항 기술 검증을 위한 VR 기반 시뮬레이션 프로그램", 2023년도 한국CDE학회 동계학술발표회, 평창, 2023.02.08-11 2023-02-09
450 Domestic Conference 박정호, 노명일, 이혜원, 조영민, 손남선, "딥 러닝 기반 다중 카메라 영상을 이용한 해상 장애물 탐지 추적에 관한 연구", 2022년도 한국항해항만학회 추계학술발표회, 부산, pp. 186, 2022.11.10 file 2022-11-10
449 Domestic Conference 정동근, 노명일, 여인창, 공민철, 김기수, 이준식, 유원철, "다양한 해상 객체를 반영한 소형 선박의 항로 계획법", 2022년도 대한조선학회 추계학술발표회, 창원, 2022.11.09-11 2022-11-11
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