AUV Obstacle Avoidance and Path Planning Based on Artificial Potential Field and Improved Reinforcement Learning

被引：0

作者：

Pan, Yunwei ^{[1
]}

Li, Min ^{[1
]}

Zeng, Xiangguang ^{[1
]}

Huang, Ao ^{[1
]}

Zhang, Jiaheng ^{[1
]}

Ren, Wenzhe ^{[1
]}

Peng, Bei ^{[2
]}

机构：

[1] School of Mechanical Engineering, Southwest Jiaotong University, Sichuan, Chengdu

[2] School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, Chengdu

来源：

Binggong Xuebao/Acta Armamentarii | 2025年 / 46卷 / 04期

关键词：

artificial potential field; autonomous underwater vehicle; obstacle avoidance; path planning; reinforcement learning;

D O I：

10.12382/bgxb.2024.0300

中图分类号：

学科分类号：

摘要：

Autonomous underwater vehicle (AUV),as one of the important underwater detection tools,are widely used in various marine military operations. Most of the existing research on AUV obstacle avoidance and path planning focuses on grid maps,and rarely considers the real maneuverability of AUVs under water. In order to solve this problem,an improved proximal policy optimization based on positive-experience retraining (PR-PPO) algorithm and an AUV obstacle avoidance and path planning method based on artificial potential field are proposed. A dynamic artificial potential field is constructed by using the sensor in AUV model and the underwater environment in the simulation software. Based on the PR-PPO reinforcement learning algorithm,the mapping relationship between the AUV state and the action is established by interacting with the environment. Real-time obstacle avoidance and path planning can be realized without dynamic model and map information. The results show that,compared with the traditional D3QN and PPO algorithms,the proposed algorithm can not only ensure the success rate of the task,but also shorten the model training time and improve the convergence effect. © 2025 China Ordnance Industry Corporation. All rights reserved.

引用

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