To solve the problem of multi-vessel collision avoid-ance of unmanned ships,a multi-vessel collision avoidance behavior decision-making method based on the deep determin-istic policy gradient(DDPG)algorithm was proposed,which combining knowledge of ship domain,international regulations for preventing collisions at sea(COLREGs),and ship ma-neuvering characteristics.The gated recurrent unit(GRU)was used to construct a neural network model and performs layer normalization,which can effectively process high-dimen-sional observation data and improve the efficiency of behavior-al decision-making methods.The reward function designed in this paper conformed to the GOLREGs,while considering the ship maneuvering habit of using small rudder angles as much as possible for avoidance.The simulation experiments of mul-tiple-ship encounters verified the advantages of the collision a-voidance decision-making method in terms of flexibility and effectiveness in this paper.
关键词
多船避碰/行为决策/国际海上避碰规则(COL-REGs)/深度强化学习/门控循环单元(GRU)
Key words
multi-ship collision avoidance/behavioral deci-sion-making/international regulations for preventing collisions at sea(COLREGs)/deep reinforcement learning/gated recur-rent unit(GRU)
维普
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