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Abstract Recently, maritime accidents caused by human factors have increased according to the increase in the number of ships for sea transportation. Therefore, the autonomous navigation systems that find appropriate avoidance routes in a complex marine environment with various obstacles is attracting attention. In this study, we proposed a collision avoidance method using deep reinforcement learning (DRL) that generates an appropriate control action. DRL-based collision avoidance method derives the required rudder angle of own ship with given state of the own ship and target ship such as position, speed, and heading. To achieve the appropriate collision avoidance, it is necessary to assess the collision risk of the target ship accurately. Therefore, the probabilistic collision risk assessment method was proposed to predict the collision risk of the target ship with the probability distribution of the data. The probability distribution was calculated through the multivariate normal distribution of the four-dimensional variables of the position, speed, and heading angle of the target ship. The collision risk of the target ship was calculated through the probability distribution for each variable and the CPA (Closest Point of Approach)-based collision risk assessment method. To verify the proposed method, we applied the DRL-based collision avoidance method and the collision risk assessment method to various scenarios. The proposed method reliably avoided collisions through flexible paths for complex situations.
Publication Date 2021-12-07

Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, "Deep Reinforcement Learning-Based Ship Collision Avoidance Considering Collision Risk", Proceedings of TEAM(Asian-Pacific Technical Exchange and Advisory Meeting on Marine Structures) 2022, Istanbul, Turkey, pp. 268, 2021.12.06-08


List of Articles
번호 분류 제목 Publication Date
476 International Conference Min-Chul Kong, Myung-Il Roh, In-Chang Yeo, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "A Detection Method of Objects with Text in Drawings Based on Deep Learning", Proceedings of ISOPE 2023, Ottawa, Canada, 2023.06.19-23 file 2023-06-22
475 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
474 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
473 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
472 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
471 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
470 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
469 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
468 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
467 Domestic Conference 노명일, "자율운항선박을 위한 핵심 AI 기술", 2023년도 스마트전기선박연구회 동계학술발표회, 대전, 2023.02.23-24 file 2023-02-23
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