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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
486 Domestic Conference 김진혁, 노명일, 여인창, "선형 설계를 위한 GNN의 적용 방안 연구", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 450, 2023.11.02-03 file 2023-11-03
485 Domestic Conference 하지상, 노명일, 공민철, 김미진, 김정연, "선박 유닛 모듈의 배관 배치 방법", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 114, 2023.11.02-03 file 2023-11-02
484 Domestic Conference 여인창, 노명일, 공민철, 유동훈, 진은석, "LIDAR를 이용한 선박의 자동 접이안 경로 생성 알고리즘", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 181, 2023.11.02-03 file 2023-11-02
483 International Conference 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 file 2023-10-20
482 Domestic Conference 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 file 2023-08-24
481 Domestic Conference 공민철, 노명일, 하지상, 김미진, 김정연, "GNN 기반 P&ID의 패턴 인식 및 분석 방법", 2023년도 대한조선학회 추계학술발표회, 울산, pp. 108, 2023.11.02-03 file 2023-11-02
480 International Conference 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 file 2023-10-19
479 Domestic Conference 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 file 2023-08-24
478 Domestic Conference 한인수, 노명일, 공민철, "딥 러닝을 활용한 P&ID 내 장비 인식 방법", 2023년도 한국CDE학회 하계학술발표회, 제주, pp. 11, 2023.08.23-26 file 2023-08-25
477 Domestic Conference 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 file 2023-08-25
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