Comprehensive Ocean Information-Enabled AUV Path Planning Via Reinforcement Learning

计算机科学 强化学习 运动规划 灵活性(工程) 水下 趋同(经济学) 人工智能 数学 经济 地质学 统计 海洋学 经济增长 机器人
作者
Meng Xi,Jiachen Yang,Jiabao Wen,Hankai Liu,Yang Li,Houbing Song
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:9 (18): 17440-17451 被引量:44
标识
DOI:10.1109/jiot.2022.3155697
摘要

The path planning of the autonomous underwater vehicle (AUV) has shown great potential in various Internet of Underwater Things (IoUT) applications. Although considerable efforts had been made, prior studies are confronted with some limitations. For one thing, existing work only uses the ocean current simulation model without introducing real ocean information, having not been supported by real data. For another, traditional path planning algorithms have strong environment dependence and lack flexibility: once the environment changes, they need to be remodeled and replanned. To overcome these challenges, this article proposes comprehensive ocean information D3QN (COID), an AUV path planning scheme exploiting comprehensive ocean information and reinforcement learning (RL), which consists of three steps. First, we introduce the comprehensive real ocean data, including weather, temperature, thermohaline, current, etc., and apply them into the regional ocean modeling system to generated reliable ocean current. Next, through well-designed state transition function and reward function, we build a 3-D grid model of ocean environment for RL. Furthermore, based on the framework of the double dueling deep $Q$ network (D3QN), COID integrates local ocean current and position features to provide state input and uses priority sampling to accelerate network convergence. The performance of COID has been evaluated and proved by numerical results, which demonstrate efficient path planning and high flexibility for expansion into different ocean environments.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
鹿立轩完成签到,获得积分10
1秒前
2秒前
Shawn完成签到,获得积分10
2秒前
小蘑菇应助哈哈哈采纳,获得10
2秒前
3秒前
4秒前
李爱国应助恋雅颖月采纳,获得10
4秒前
5秒前
留白发布了新的文献求助10
5秒前
fdkufghkd完成签到,获得积分10
8秒前
9秒前
9秒前
懵懂的幻桃完成签到 ,获得积分10
9秒前
flyfish完成签到,获得积分10
10秒前
10秒前
上官若男应助斯文莺采纳,获得30
11秒前
12秒前
12秒前
13秒前
Yun发布了新的文献求助10
13秒前
13秒前
13秒前
kyra发布了新的文献求助10
14秒前
xiaoze发布了新的文献求助10
14秒前
15秒前
15秒前
小蘑菇应助Yana1311采纳,获得10
16秒前
16秒前
傻傻的小刺猬完成签到,获得积分10
16秒前
16秒前
16秒前
ColdSunWu发布了新的文献求助10
17秒前
小坤同学发布了新的文献求助10
17秒前
132发布了新的文献求助30
18秒前
轻松小张完成签到,获得积分10
18秒前
量子星尘发布了新的文献求助10
18秒前
shilong.yang发布了新的文献求助10
19秒前
YamDaamCaa应助kk采纳,获得30
19秒前
超帅的不尤完成签到,获得积分20
19秒前
桐桐应助mariawang采纳,获得10
20秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988786
求助须知:如何正确求助?哪些是违规求助? 3531116
关于积分的说明 11252493
捐赠科研通 3269766
什么是DOI,文献DOI怎么找? 1804771
邀请新用户注册赠送积分活动 881870
科研通“疑难数据库(出版商)”最低求助积分说明 809021