AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning

AUV Obstacle Avoidance Planning Based on Deep Reinforcement Learning

In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method b...

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Bibliographic Details
Main Authors: Jianya Yuan, Hongjian Wang, Honghan Zhang, Changjian Lin, Dan Yu, Chengfeng Li
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Journal of Marine Science and Engineering
Subjects:
autonomous underwater vehicle (AUV)
collision avoidance planning
deep reinforcement learning (DRL)
double-DQN (D-DQN)
Online Access:https://www.mdpi.com/2077-1312/9/11/1166

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