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Abstract Developing a high-level autonomous collision avoidance system for ships which can operate in an unstructured and unpredictable environment is a challenging task. Especially in the congested sea areas, each ship should continuously make decisions to avoid collisions with many other ships in the busy and complex waterway. Furthermore, recent reports indicate that a large number of collision accidents at sea are caused by or related to human decision failures with lack of situational awareness and failure to comply with International Regulations for Preventing Collisions at Sea (COLREGs). In this study, we propose a robust and efficient method to collision avoidance problems of multi-ships based on the deep reinforcement learning (DRL) algorithm. The proposed method directly maps the states of encountered ships to an ownship’s steering commands in terms of the rudder angle using a deep neural network (DNN). This DNN is trained over multi-ships on rich encountering situations using the policy gradient based DRL algorithm. To handle multiple encountered ships, we classify them into four regions based on COLREGs, and only consider the nearest ship in each region. We validate the proposed method in a variety of simulated scenarios thorough performance evaluations. The result shows that the proposed method can find time efficient, collision-free paths for multi-ships. Also, it shows that the proposed method has excellent adaptability to unknown complex environments.
Publication Date 2019-09-24

Luman Zhao, Myung-Il Roh, Hye-Won Lee, Do-Hyun Chun, Sung-Jun Lee, "A Collision Avoidance Method of Multi-ships Based on Deep Reinforcement Learning Considering COLREGs", Proceedings of ICCAS(International Conference on Computer Applications in Shipbuilding) 2019, Rotterdam, Netherlands, pp. 85-88, 2019.09.24-26


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번호 분류 제목 Publication Date
416 Domestic Conference 공민철, 노명일, 박정호, "가상 현실 기반의 선박 충돌 시나리오 구현", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-10
415 International Conference Jisang Ha, Myung-Il Roh, Ki-Su Kim, Min-Chul Kong, "Integrated Method for the Arrangement Design of a Ship for Implementing Digital Twin in Design", Proceedings of PRADS 2022, Dubrovnik, Croatia, 2022.10.09-13 file 2022-10-10
414 Domestic Conference 김진혁, 노명일, 김기수, 여인창, "딥 러닝을 이용한 소형 선박의 성능 예측 방법", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-10
413 Domestic Conference 김기수, 노명일, 함승호, 하솔, "해난 사고 시 승객의 탈출 해석을 위한 다차원 행동 모델", 2022년도 한국CDE학회 동계학술발표회, 제주, 2022.02.09-12 file 2022-02-10
412 Domestic Conference 조영민, 노명일, 이혜원, 진은석, 유동훈, "다중 센서 융합을 이용한 주위 선박의 경로 추적 방법", 2021년도 대한조선학회 추계학술발표회, 군산, pp. 509, 2021.11.04-05 file 2021-11-04
411 Domestic Conference 조영민, 노명일, 이혜원, 진은석, 유동훈, "가상 환경에서의 선박 추적을 위한 AIS 및 RADAR 데이터 융합", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 307, 2021.08.25-28 file 2021-08-25
410 Domestic Conference 송하민, 노명일, 김기수, "차세대 함정을 위한 승조원 규모 및 배치 최적화 방법", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 428, 2021.08.25-28 file 2021-08-25
409 Domestic Conference 정동근, 노명일, 김기수, 이준식, 김대혁, 장왕석, "연안 항해용 소형 선박을 위한 경로 계획 알고리즘", 2021년도 한국CDE학회 하계학술발표회, 제주, pp. 426, 2021.08.25-28 file 2021-08-25
408 Domestic Conference 김기수, 노명일, "손상된 선박의 침수에 따른 자세 변화를 고려한 승객 탈출 행동 모델", 2021년도 대한조선학회 추계학술발표회, 군산, pp. 546, 2021.11.04-05 file 2021-11-04
407 Domestic Conference 하지상, 노명일, 김기수, "전문가 시스템을 활용한 최적 장비 배치 및 배관 경로 생성 방법", 2021년도 대한조선학회 추계학술발표회, 군산, pp. 654, 2021.11.04-05 file 2021-11-05
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