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2019.08.06 15:07

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 2019, Rotterdam, Netherlands, pp. 85-88, 2019.09.24-26

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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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  1. No Image 08Sep
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    Min-Jae Oh, Myung-Il Roh, "Hull Form Surface Generation Using T-Spline", Proceedings of the 31st Asian-Pacific TEAM 2017, Osaka, Japan, 2017.09.25-28

  2. No Image 18Feb
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    Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jongoh Kim, Hogyun Park, Jeongyoul Lee, "A Method of Variable Recognition and Connection for Reviewing Ship Regulations", Proceedings of ICCAS 2022, Yokohama, Japan, pp. 171-175, 2022.09.13-15

  3. No Image 30Mar
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    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

  4. No Image 17Dec
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    Min-Chul Kong, Myung-Il Roh, Jisang Ha, Eun Seok Jin, Donghun Yu, "Design of the Integrated System for the Safe Operation Based on Augmented Reality", Proceedings of PRADS 2022, Dubrovnik, Croatia, 2022.10.09-13

  5. No Image 13Feb
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    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

  6. No Image 17Jun
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    in Conference Chairman

    Mechanics & Collision I, ISOPE(International Society of Offshore and Polar Engineers) 2017, San Francisco, USA, 2017.06.25-30

  7. No Image 05Oct
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    in International Conference

    Luman Zhao, Myung-Il Roh, Seung-Ho Ham, "Hardware-In-the-Loop Simulation for the Design and Testing of an Active Compensation System on an Offshore Supply Vessel", Proceedings of SUTTC 2016, Beijing, China, 2016.09.26-30

  8. No Image 18Apr
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    Luman Zhao, Myung-Il Roh, Seung-Ho Ham, "Hardware-In-the-Loop Simulation for a Heave Compensator of an Offshore Support Vessel", Proceedings of OMAE 2016, Busan, Korea, pp. 1-5, 2016.06.19-24

  9. No Image 04Jul
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    Luman Zhao, Myung-Il Roh, Seung-Ho Ham, "Anti-sway Control of the Crane on an Offshore Support Vessel Based on the Hardware-In-the-Loop Simulation", Proceedings of ISOPE 2017, San Francisco, USA, pp. 651-654, 2017.06.25-30

  10. No Image 06Aug
    by SyDLab
    in International Conference

    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 2019, Rotterdam, Netherlands, pp. 85-88, 2019.09.24-26

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