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Abstract Developing a high-level autonomous collision avoidance system for ships that can operate in an unstructured and unpredictable environment is challenging. Particularly in congested sea areas, each ship should make decisions continuously to avoid collisions with other ships in a busy and complex waterway. Furthermore, recent reports indicate that a large number of marine collision accidents are caused by or are related to human decision failures concerning a lack of situational awareness and failure to comply with the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). In this study, we propose an efficient method to overcome multiship collision avoidance problems based on the Deep Reinforcement Learning (DRL) algorithm by expanding our previous study (Zhao et al., 2019). The proposed method directly maps the states of encountered ships to an ownship's steering commands in terms of rudder angle using the Deep Neural Network (DNN). This DNN is trained over multiple ships in rich encountering situations using the policy-gradient based DRL algorithm. To address multiple encountered ships, we classify them into four regions based on COLREGs, and consider only the nearest ship in each region. We validate the proposed collision avoidance method in a variety of simulated scenarios with thorough performance evaluations, and demonstrate that the final DRL controller can obtain time efficient and collision-free paths for multiple ships. Simulation results indicate that multiple ships can avoid collisions with each other while following their own predefined paths simultaneously. In addition, the proposed approach demonstrates its excellent adaptability to unknown complex environments with various encountered ships.
Publication Date 2019-11-01
Role Corresponding Author
Category SCI
Impact Factor 1.894

Luman Zhao, Myung-Il Roh, "COLREGs-Compliant Multiship Collision Avoidance Based on Deep Reinforcement Learning", Ocean Engineering, Vol. 191, pp. 1-15, 2019.11.01


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  2. Myung-Il Roh, Kyu-Yeul Lee, "Generation of the Production Material Information of a Building Block and Simulation of the Block Erection for Computer-Aided Process Planning and Scheduling in Shipbuilding", International Journal of Production Research, Vol. 45, No. 20, pp. 4653-4683, 2007.10.01

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  3. Myung-Il Roh, Kyu-Yeul Lee, "Generation of the 3D CAD Model of the Hull Structure at the Initial Ship Design Stage and Its Application", Computers in Industry, Vol. 58, No. 6, pp. 539-557, 2007.08.01

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  5. Myung-Il Roh, Kyu-Yeul Lee, "An Initial Hull Structural Modeling System for Computer-Aided Process Planning in Shipbuilding", Advances In Engineering Software, Vol. 37, No. 7, pp. 457-476, 2006.07.01

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  7. Myung-Il Roh, "Determination of an Economical Shipping Route Considering the Effects of Sea State for Lower Fuel Consumption", International Journal of Naval Architecture and Ocean Engineering, Vol. 5, No. 2, pp. 246-262, 2013.06.01

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  8. Myung-Il Roh, "Block Layout Method in the Block Stockyard Based on the Genetic Algorithm", Ocean Systems Engineering, Vol. 2, No. 4, pp. 271-287, 2012.12.01

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  9. Min-Jae Oh, Taw-Wan Kim, Myung-Il Roh, "G1 Bezier Surface Interpolation with T-junctions at a 3-Valent Singular Vertex", Computer Aided Geometric Design, Vol. 67, pp. 79-95, 2018.12.01

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  10. Min-Jae Oh, Myung-Il Roh, Tae-Wan Kim, "Local T-spline Surface Skinning with Shape Preservation", Computer-Aided Design, Vol. 104, pp. 15-26, 2018.11.01

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  11. Min-Jae Oh, Myung-Il Roh, Sung-Woo Park, Do-Hyun Chun, Se-Hyun Myung, "Big Data-based Piping Material Analysis Framework in Offshore Structure for Contract Design", Ocean Systems Engineering, Vol. 9, No. 1, pp. 79-95, 2019.01.01

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  12. Min-Jae Oh, Myung-Il Roh, Sung-Woo Park, Do-Hyun Chun, Myeong-Jo Son, Jeong-Youl Lee, “Operational Analysis of Container Ships by Using Maritime Big Data”, Journal of Marine Science and Engineering, Vol. 9, No. 4, pp. 438: 1-21, 2021.04.18

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  13. Min-Chul Kong, Myung-Il Roh, Ki-Su Kim, Jeongyoul Lee, Jongoh Kim, Gapheon Lee, "Object Detection Method for Ship Safety Plans Using Deep Learning", Ocean Engineering, 2022.02.15

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  14. Luman Zhao, Myung-Il Roh, “A Thrust Allocation Method for Efficient Dynamic Positioning of a Semisubmersible Drilling Rig Based on the Hybrid Optimization Algorithm”, Mathematical Problems in Engineering, Vol. 2015, pp. 1-12, 2015.10.01

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  15. Luman Zhao, Myung-Il Roh, Sung-Jun Lee, "Control Method for Path Following and Collision Avoidance of Autonomous Ships Based on Deep Reinforcement Learning", Journal of Marine Science and Technology (Taiwan), Vol. 27, No. 4, pp. 293-310, 2019.08.01

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  16. Luman Zhao, Myung-Il Roh, Seung-Ho Ham, "Performance Analysis of an Active Heave Compensation System on an Offshore Supply Vessel Using the Hardware-In-The-Loop Simulation", Journal of Marine Science and Technology (Taiwan), Vol. 26, No. 5

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  17. 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", International Journal of Offshore and Polar Engineering, Vol. 28, No. 2, pp. 182-189, 2018.06.01

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  18. Luman Zhao, Myung-Il Roh, Jeong-Woo Hong, "Optimal Thrust Allocation for Dynamic Positioning of Deep-sea Working Vessel", Journal of Advanced Research in Ocean Engineering, Vol. 1, No. 2, pp. 94-105, 2015.06.01

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  19. Luman Zhao, Myung-Il Roh, "COLREGs-Compliant Multiship Collision Avoidance Based on Deep Reinforcement Learning", Ocean Engineering, Vol. 191, pp. 1-15, 2019.11.01

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  20. Kyu-Yeul Lee, Won-Joon Lee, Myung-Il Roh, "Development of a Semantic Product Modeling System for Initial Hull Structure in Shipbuilding", Robotics and Computer-Integrated Manufacturing Journal, Vol. 20, No. 3, pp. 211-223, 2004.06.01

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