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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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373 Domestic Conference 이준범, 노명일, 김기수, 한기민, 이갑헌, "딥 러닝 기반 해기상 및 선박 소요 마력 예측", 2020년도 한국CDE학회 동계학술발표회, 평창, pp. 343, 2020.02.05-08 file 2020-02-07
372 Domestic Conference 이원재, 노명일, 이성준, 하지상, 공민철, 이종혁, "선박의 주변 인지를 위한 영상 인식 기반 물체 탐지 및 추적 방법", 2020년도 한국CDE학회 동계학술발표회, 평창, pp. 341, 2020.02.05-08 file 2020-02-07
371 International Conference Jisang Ha, Myung-Il Roh, Jong-Hyeok Lee, Jin-Hyeok Kim, Min-Chul Kong, Seung-Ho Ham, "Integrated Ship Remote Operating System Based on Digital Twin Technology", Proceedings of TEAM 2019, Tainan, Taiwan, pp. 106, 2019.10.14-17 file 2019-10-16
370 Domestic Conference 이준범, 노명일, 김기수, 김상엽, 한기민, 김대현, "딥 러닝 기법을 이용한 선박의 소요 마력 예측", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 523, 2019.10.24-26 file 2019-10-25
369 Conference Chairman Computational Design and Optimization 4, ACDDE(Asian Conference on Design and Digital Engineering) 2018, Okinawa, Japan, 2018.11.01-03 2018-11-02
368 Domestic Conference 이준범, 노명일, 김기수, 손명조, 한기민, 김대헌, "딥 러닝 기반 해기상 및 소요 마력 예측", 2019년도 한국CDE학회 하계학술발표회, 제주, pp. 235, 2019.08.19-22 file 2019-08-21
367 Domestic Conference 김진혁, 노명일, 공민철, "부분 재액화 시스템의 유지 및 보수를 위한 증강 현실 컨텐츠 개발", 2019년도 한국CDE학회 하계학술발표회, 제주, pp. 269, 2019.08.19-22 file 2019-08-21
366 Domestic Conference 이혜원, 노명일, 함승호, 전도현 "블록의 탑재 제어를 위한 갠트리 크레인의 제어기 설계 고도화", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 139, 2019.10.24-26 file 2019-10-24
365 Domestic Conference 전도현, 노명일, 이혜원, 하지상, 진은석, 김인일, "딥 러닝 기반 자율 운항 선박의 충돌 회피 방법 연구", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 429, 2019.10.24-26 file 2019-10-24
364 Domestic Conference 김진혁, 노명일, 공민철, "증강 현실 기술을 이용한 장비의 원격 운영 및 유지 보수 방법", 2019년도 대한조선학회 추계학술발표회, 경주, pp. 196, 2019.10.24-26 file 2019-10-24
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