A Novel Ship Collision Avoidance Awareness Approach for Cooperating Ships Using Multi-Agent Deep Reinforcement Learning
AuthorsChen, Chen; orcid: 0000-0003-1064-4961; email: firstname.lastname@example.org
Ma, Feng; orcid: 0000-0002-1357-3006; email: email@example.com
Xu, Xiaobin; email: firstname.lastname@example.org
Chen, Yuwang; orcid: 0000-0002-2007-1821; email: Yu-wang.Chen@manchester.ac.uk
Wang, Jin; email: J.Wang@ljmu.ac.uk
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AbstractShips are special machineries with large inertias and relatively weak driving forces. Simulating 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.
CitationJournal of Marine Science and Engineering, volume 9, issue 10, page e1056
DescriptionFrom MDPI via Jisc Publications Router
History: accepted 2021-09-19, pub-electronic 2021-09-25
Publication status: Published
Funder: Zhejiang Province Key Ramp;D projects,China; Grant(s): 2021C03015, R21F030005
Funder: NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization; Grant(s): U1709215
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