Article(id=1314908053921694337, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907935172551117, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2026.03.010, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1772294400000, receivedDateStr=2026-03-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791428109865, onlineDateStr=2026-10-08, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791428109865, onlineIssueDateStr=2026-10-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791428109865, creator=13701087609, updateTime=1791428109865, updator=13701087609, issue=Issue{id=1314907935172551117, tenantId=1146029695717560320, journalId=1235980550691926019, year='2026', volume='46', issue='3', pageStart='1', pageEnd='297', issueExtLink='null', onlineDate='null', pubDate='1780243200000', pubDateStr='2026-06-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791428081553, creator='13701087609', updateTime=1791430444568, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1314917846770082179, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907935172551117, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1314917846770082180, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1314907935172551117, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=82, endPage=90, ext={EN=ArticleExt(id=1314908055796548226, articleId=1314908053921694337, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=A Positioning Data Prediction Method for Deep-Sea Mining Vehicles Based on Physics-Informed Neural Networks, columnId=1314908041200362409, journalTitle=Mining and Metallurgical Engineering, columnName=SPECIAL TOPIC ON BREAKTHROUGHS IN KEY TECHNOLOGIES AND COORDINATED DEVELOPMENT OF INDUSTRIAL CHAINS FOR DEEP-SEA MINERAL RESOURCE EXPLOITATION, runingTitle=null, highlight=null, articleAbstract=

To improve the positioning accuracy and real-time performance of deep-sea mining equipment in complex nonlinear environments, a positioning data prediction algorithm was proposed based on Physics-Informed Neural Network (PINN). This method introduces the kinematic equations of deep-sea mining vehicle into the neural network structure, and achieves the synergistic fitting of sensor data and physical models by constructing a composite loss function integrating physical constraints. Furthermore, two test scenarios under different working conditions were constructed on the Gazebo simulation platform to systematically compare the prediction performance between the proposed PINN and a traditional Feedforward Neural Network (FNN). The results show that, compared with the FNN model, the PINN model reduces the mean absolute error (MAE) from 0.621 3 m to 0.128 3 m, the root mean square error（RMSE）from 0.674 0 m to 0.137 0 m, and the maximum error from 0.938 6 m to 0.196 2 m in working condition 1; while in working condition 2, the MAE is reduced from 0.823 9 m to 0.335 4 m, the RMSE from 0.935 2 m to 0.379 5 m, and the maximum error from 1.585 8 m to 0.640 2 m. PINN demonstrates significant advantages in extreme error control and robustness, exhibiting lower sensitivity to outliers under complex working conditions in particular.

, authors=Zelong ZHOU^{1, 2}, Yangrui CHENG^{1, 2}, Guiting MAO^{1, 2}, Saifeng PENG^{1, 2}, authorsList=Zelong ZHOU, Yangrui CHENG, Guiting MAO, Saifeng PENG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1314908056320836227, articleId=1314908053921694337, tenantId=1146029695717560320, journalId=1235980550691926019, language=CN, title=基于物理信息神经网络的深海采矿车定位数据预测方法, columnId=1314908041376523179, journalTitle=矿冶工程杂志, columnName=深海矿产资源开发关键技术突破与产业链协同发展专题, runingTitle=null, highlight=null, articleAbstract=

为了提高深海采矿装备在复杂非线性环境中的定位精度与实时性，提出了一种基于物理信息神经网络（PINN）的定位数据预测算法。该方法在神经网络结构中引入深海采矿车的运动学方程，通过构建融合物理约束的复合损失函数，实现了对传感器数据与物理模型的协同拟合。在Gazebo仿真平台上构建了2个不同工况的测试场景，系统对比了PINN与传统前馈神经网络（FNN）的预测性能。结果表明，相比FNN模型，PINN模型在工况1中，平均绝对误差（MAE）由0.621 3 m降至0.128 3 m，均方根误差（RMSE）由0.674 0 m降至0.137 0 m，最大误差由0.938 6 m降至0.196 2 m；在工况2中，MAE由0.823 9 m降至0.335 4 m，RMSE由0.935 2 m降至0.379 5 m，最大误差由1.585 8 m降至0.640 2 m。PINN在极端误差控制与鲁棒性方面表现出明显优势，尤其在复杂工况下对异常值的敏感度更低。

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^2．深海矿产资源开发利用技术国家重点实验室，湖南 长沙 410012, bio={"content":"

周泽龙（2000—），男，湖南长沙人，硕士研究生，主要研究方向为深海采矿车导航定位。E-mail: 1061250024@qq.com

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周泽龙（2000—），男，湖南长沙人，硕士研究生，主要研究方向为深海采矿车导航定位。E-mail: 1061250024@qq.com

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