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A novel ship collision avoidance awareness approach for cooperating ships using multi-agent deep reinforcement learning

Chen, C, Ma, F, Xu, X, Chen, Y and Wang, J (2021) A novel ship collision avoidance awareness approach for cooperating ships using multi-agent deep reinforcement learning. Journal of Marine Science and Engineering, 9 (10). ISSN 2077-1312

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Ships are special machineries with large inertias and relatively weak driving forces. Sim-ulating the manual operations of manipulating ships with artificial intelligence (AI) and machine learning techniques becomes more and more common, in which avoiding collisions in crowded waters may be the most challenging task. This research proposes a cooperative collision avoidance approach for multiple ships using a multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, each ship is modeled as an individual agent, controlled by a Deep Q-Network (DQN) method and described by a dedicated ship motion model. Each agent observes the state of itself and other ships as well as the surrounding environment. Then, agents analyze the navigation situation and make motion decisions accordingly. In particular, specific reward function schemas are designed to simulate the degree of cooperation among agents. According to the International Regulations for Preventing Collisions at Sea (COLREGs), three typical scenarios of simulation, which are head-on, overtaking and crossing, are established to validate the proposed approach. With sufficient training of MADRL, the ship agents were capable of avoiding collisions through cooperation in narrow crowded waters. This method provides new insights for bionic modeling of ship operations, which is of important theoretical and practical significance.

Item Type: Article
Uncontrolled Keywords: 0405 Oceanography, 0704 Fisheries Sciences, 0911 Maritime Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Engineering
Publisher: MDPI
Date Deposited: 13 Oct 2021 13:21
Last Modified: 13 Oct 2021 13:21
DOI or ID number: 10.3390/jmse9101056
URI: https://researchonline.ljmu.ac.uk/id/eprint/15637
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