Abstract
The congestion of waterways can easily lead to traffic hazards. Moreover, according to the data, the majority of sea collisions are caused by human error and the failure to comply with the Convention on the International Regulation for the preventing Collision at Sea (COLREGs). To avoid this situation, ship automatic collision avoidance has become one of the most important research issues in the field of marine engineering. In this study, an efficient method is proposed to solve multi-ship collision avoidance problems based on the multi-agent reinforcement learning (MARL) algorithm. Firstly, the COLREGs and ship maneuverability are considered for achieving multi-ship collision avoidance. Subsequently, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is utilized to detect and reduce the risk of collision. Ships can operate at the safe velocity computed by the ORCA algorithm to avoid collisions. Finally, the Nomoto three-degrees-of-freedom (3-DOF) model is used to simulate the maneuvers of ships. According to the above information and algorithms, this study designs and improves the state space, action space and reward function. For validating the effectiveness of the method, this study designs various simulation scenarios with thorough performance evaluations. The simulation results indicate that the proposed method is flexible and scalable in solving multi-ship collision avoidance, complying with COLREGs in various scenarios.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Dalian Innovation Team Support Plan in the Key Research Field
Subject
Ocean Engineering,Water Science and Technology,Civil and Structural Engineering
Reference31 articles.
1. Guan, W., Peng, H., Zhang, X., and Sun, H. (2022). Ship Steering Adaptive CGS Control Based on EKF Identification Method. J. Mar. Sci. Eng., 10.
2. Autonomous ship collision avoidance navigation concepts, technologies and techniques;Statheros;J. Navig.,2008
3. Deep reinforcement learning overview: The development of computer go;Zhao;Control. Theory Appl.,2016
4. A brief overview of deep reinforcement learning;Liu;Chin. J. Comput.,2018
5. Imagenet large scale visual recognition challenge;Russakovsky;Int. J. Comput. Vision,2015
Cited by
9 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献