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Abstract Social interest in autonomous navigation systems for autonomous ships is also increasing. For a robust autonomous navigation system, the location, speed, and direction of the ship and other ships must be identified in real time, and collision avoidance should be performed at an appropriate time by considering the collision risk. In this study, we proposed a collision avoidance method that quantitatively assesses the collision risk and then generates an avoidance path. First, to assess the collision risk, a collision risk assessment method based on the ship domain and the closest point of approach (CPA) was proposed. The ship domain is created with an asymmetric shape considering manoeuvring performance and the COLREGs. The CPA is used to assess quantitative collision risk value. Subsequently, a path generation algorithm based on deep reinforcement learning (DRL) was proposed to determine the avoidance time and to generate an avoidance path complying the COLREGs for the most dangerous ship in terms of collision risk. The information of own ship and target ship such as location, speed, heading, collision risk is used as the input state, and the rudder angle of own ship is set as the output action of the DRL. The cost function related to the path following and the collision avoidance is defined as the reward of the DRL-based collision avoidance method. Additionally, the DRL modes are defined to navigate the flexible avoidance path by changing the ratio between the path following and the collision avoidance. To verify the proposed method, we compared the collision avoidance method with the A* algorithm, which is a traditional path planning algorithm, and analyzed the results for various scenarios. The proposed method reliably avoided collisions through flexible paths for complex and unexpected changes in situations compared to the A* algorithm.
Publication Date 2021-08-15
Role Corresponding Author
Category SCI

Do-Hyun Chun, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Donghun Yu, “Deep Reinforcement Learning-based Collision Avoidance for an Autonomous Ship”, Ocean Engineering, Vol. 234, pp. 1-20, 2021.08.15

https://doi.org/10.1016/j.oceaneng.2021.109216


List of Articles
번호 분류 제목 Publication Date
6 International Journal Sung-Min Lee, Myung-Il Roh, Ki-Su Kim, Hoe-Ryong Jung, Jong-Jin Park, "Method for a Simultaneous Determination of the Path and the Speed for Ship Route Planning Problems", Ocean Engineering, Vol. 157, pp. 301-312, 2018.06.01 file 2018-06-01
5 International Journal Sung-Min Lee, Myung-Il Roh, Ki-Su Kim, Seung-Ho Ham, "Optimum Design of Lug Arrangement Based on Static and Dynamic Analyses for Block Lifting", Journal of Ship Production and Design, Vol. 34, No. 2, pp. 119-133, 2018.05.01 file 2018-05-01
4 International Journal Sung-Woo Park, Myung-Il Roh, Min-Jae Oh, Seong-Hoon Kim, "Association Analysis of Piping Materials of an Offshore Structure Using Big Data Technology", Journal of Ship Production and Design, Vol. 35, No. 3, pp. 220-230, 2019.08.01 file 2019-08-01
3 International Journal Tae-Sub Um, Myung-Il Roh, "Optimal Dimension Design of a Hatch Cover for Lightening a Bulk Carrier", International Journal of Naval Architecture and Ocean Engineering, Vol. 7, No. 2, pp. 270-287, 2015.03.01 file 2015-03-01
2 International Journal Won-Jae Lee, Myung-Il Roh, Hye-Won Lee, Jisang Ha, Yeong-Min Cho, Nam-Sun Son, “Detection and Tracking for the Awareness of Surroundings of a Ship Based on Deep Learning”, Journal of Computational Design and Engineering, Vo. 8, No. 5 file 2021-10-01
1 International Journal Xing Li, Myung-Il Roh, Seung-Ho Ham, "A Collaborative Simulation in Shipbuilding and the Offshore Installation Based on the Integration of the Dynamic Analysis, Virtual Reality, and Control Devices", IJNAOE, Vol. 11, No. 2, pp. 699-722, 2019.07.01 file 2019-07-01
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