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
云泥完成签到,获得积分10
1秒前
1秒前
隐形曼青应助okface采纳,获得10
2秒前
zhang完成签到,获得积分10
3秒前
hopen完成签到 ,获得积分10
4秒前
丘比特应助SS采纳,获得10
6秒前
月亮moon完成签到,获得积分10
6秒前
7秒前
充电宝应助qqq采纳,获得10
10秒前
顾矜应助lihan采纳,获得10
10秒前
爆米花应助周em12_采纳,获得10
11秒前
12秒前
三物完成签到 ,获得积分10
13秒前
jolt发布了新的文献求助10
14秒前
16秒前
16秒前
17秒前
momo发布了新的文献求助10
18秒前
量子星尘发布了新的文献求助30
18秒前
19秒前
20秒前
20秒前
善学以致用应助笃定采纳,获得30
21秒前
SS发布了新的文献求助10
23秒前
23秒前
ZZZ发布了新的文献求助10
23秒前
Justtry发布了新的文献求助20
25秒前
25秒前
顾矜应助momo采纳,获得10
26秒前
26秒前
hnlgdx发布了新的文献求助20
26秒前
28秒前
重要冷之完成签到,获得积分20
28秒前
28秒前
31秒前
VV完成签到,获得积分10
31秒前
ccalvintan发布了新的文献求助10
32秒前
蛋挞发布了新的文献求助10
33秒前
小马甲应助SS采纳,获得10
36秒前
XX完成签到,获得积分10
39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 3989297
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253893
捐赠科研通 3270097
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809158