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

Pub Date : 2023-10-26 DOI:10.1007/s10015-023-00909-4
Ryosuke Saga, Rinto Kozono, Yutaro Tsurumi, Yasunori Nihei
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Abstract

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.

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基于深度强化学习的自动驾驶船舶避障路线规划
与现有的海洋航线优化研究相比,本文提出的方法能够在考虑避障的情况下生成短程航线,并显著缩短计算时间。计算时间缩短后,可以重新计算自主航行船只的航线。通过模拟四种可能需要重新计算的航行中船只的情况,本文证明了所提出的方法可以为因某些因素而需要改变航线的船只生成新的和更优的航线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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