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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
476 Domestic Conference 김동우, 노명일, 전도현, 우선홍, 김진혁, 김용태, 이혜원, "멤브레인형 액화가스 화물창 1차방벽 최적 형상 개발을 위한 딥러닝 기반 구조 안전성 예측 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 22-23, 2023.05.02-04 file 2023-05-03
475 Domestic Conference 김하연, 노명일, 하지상, 조영민, 이혜원, "센서 데이터를 활용한 해상 장애물의 개선된 추적 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 436, 2023.05.02-04 file 2023-05-04
474 Domestic Conference 하지상, 노명일, 공민철, 김기수, "장비 및 배관의 다단계 최적화를 활용한 선박의 기관실 배치 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 311, 2023.05.02-04 file 2023-05-02
473 Domestic Conference 김진혁, 노명일, 여인창, "설계 요구 조건을 고려한 MLP 기반 상선의 선형 변환 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 309-310, 2023.05.02-04 file 2023-05-04
472 Domestic Conference 조영민, 노명일, 전도현, 하지상, 이혜원, 유동훈, 진은석, "개선된 센서 데이텨 연관 및 융합 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 261, 2023.05.02-04 file 2023-05-03
471 Domestic Conference 공민철, 노명일, 한인수, 김미진, 김정연, "P&ID 내 객체 및 문자 인식 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 313-314, 2023.05.02-04 file 2023-05-04
470 Domestic Conference 여인창, 노명일, 공민철, 전도현, 하지상, 유동훈, 진은석, "선박의 자동 접이안을 위한 서라운드 뷰 생성 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp.315-316, 2023.05.02-04 file 2023-05-04
469 Domestic Conference 전도현, 노명일, 이혜원, 유동훈, 진은석 "입력 데이터의 불확실성과 복잡한 조우 상황을 고려한 충돌 위험도 평가 방법", 2023년도 대한조선학회 춘계학술발표회, 부산, pp. 442, 2023.05.02-04 file 2023-05-04
468 Domestic Conference 노명일, "자율운항선박을 위한 핵심 AI 기술", 2023년도 스마트전기선박연구회 동계학술발표회, 대전, 2023.02.23-24 file 2023-02-23
467 Domestic Conference 김동우, 노명일, 전도현, 우선홍, 이혜원, 김용태, "딥 러닝을 이용한 멤브레인 타입 LNG선 화물창의 1차 방벽의 형상 최적화 방법 ", 2023년도 한국CDE학회 동계학술발표회, 평창, pp. 275, 2023.02.08-11 file 2023-02-10
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