Deep-reinforcement learning-based route planning with obstacle avoidance for autonomous vessels

被引：1

作者：

Saga, Ryosuke ^{[1
]}

Kozono, Rinto ^{[2
]}

Tsurumi, Yutaro ^{[3
]}

Nihei, Yasunori ^{[4
]}

机构：

[1] Osaka Metropolitan Univ, Grad Sch Informat, Osaka, Japan

[2] Osaka Prefecture Univ, Grad Sch Humanities & Sustainable Syst Sci, Osaka, Japan

[3] Osaka Prefecture Univ, Grad Sch Engn, Osaka, Japan

[4] Osaka Metropolitan Univ, Grad Sch Engn, Osaka, Japan

来源：

ARTIFICIAL LIFE AND ROBOTICS | 2024年 / 29卷 / 01期

关键词：

Autonomous vessel; Optimal route finding; Attention model; Machine learning; Intelligent control;

D O I：

10.1007/s10015-023-00909-4

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

This paper proposes a method to enables the generation of short-length routes with consideration of obstacle avoidance and significantly reduces the computation time compared to existing research for ocean route optimization. The reduced computation time allows recalculation of routes for autonomous vessel underway. By simulating the recalculation of four cases of the vessel underway that may require recalculation, this paper demonstrates that the proposed method can generate new and superior routes for the vessel that needs to change their routes due to certain factors.

引用

页码：136 / 144

页数：9

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