Article(id=1325193105892311897, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193025059692970, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2025.01.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1732982400000, receivedDateStr=2024-12-01, revisedDate=1736006400000, revisedDateStr=2025-01-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1793880257341, onlineDateStr=2026-11-05, pubDate=1739548800000, pubDateStr=2025-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793880257341, onlineIssueDateStr=2026-11-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793880257341, creator=13701087609, updateTime=1793880257341, updator=13701087609, issue=Issue{id=1325193025059692970, tenantId=1146029695717560320, journalId=1319690209941618849, year='2025', volume='6', issue='1', pageStart='1', pageEnd='110', issueExtLink='null', onlineDate='null', pubDate='1739548800000', pubDateStr='2025-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793880238070, creator='13701087609', updateTime=1793943677543, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325459110036112192, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193025059692970, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325459110036112193, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1325193025059692970, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=81, endPage=90, ext={EN=ArticleExt(id=1325193106454348634, articleId=1325193105892311897, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Research on slip model of automatic driving of shellfish harvesting equipment in mudflat, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In view of the problem of relative displacement caused by wheel slippage during the operation of autonomous wheeled clam harvesting equipment on intertidal mudflats, which affects the path tracking accuracy of the autonomous driving system, a slip prediction method based on vehicle kinematics and dynamics models was proposed. The vehicle's motion trajectory was derived according to the four-wheel chassis kinematic model, and the walking dynamics model was established by incorporating tire lateral and longitudinal force characteristics. The calculation methods for longitudinal slip and lateral slip were clarified. An autonomous driving test platform was designed, consisting of perception, planning, and control layers. The platform collected position, steering angle, and wheel speed data using an RTK-GNSS system and relevant sensors. Longitudinal and lateral slip experiments were conducted to investigate the slip characteristics of the harvesting equipment in the intertidal environment. The longitudinal slip experiment analyzed the effects of motor PWM (pulse width modulation) and load on slip ratio, and a longitudinal slip model was established through data fitting. The lateral slip experiment explored the slip characteristics under different steering angles, leading to the fitting of a lateral slip model. To simplify calculations, the slip coefficient function was expressed in the form of a Taylor polynomial. The model's accuracy was validated through experiments, with the error between the actual and predicted trajectories being less than 16%, significantly reducing slip-related interference errors in autonomous driving on mudflats. Through experimental analysis and model optimization, this study improves the accuracy of slip prediction and provides a reference for achieving high-precision navigation for clam harvesting equipment in soft ground environments. Future research could focus on real-time parameter estimation of the slip model and the dynamics modeling of ground-contact components to enhance the equipment.

, authors=Zhilei ZHANG¹, Feihong GUO¹, Bingyang ZHOU¹, Bin XU¹, Hao WU¹, Gang MU^{1, 2, 3}, authorsList=Zhilei ZHANG, Feihong GUO, Bingyang ZHOU, Bin XU, Hao WU, Gang MU, authorCompany=null, correspAuthors=Gang MU, authorNote=, correspAuthorsNote=, 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=1325193107192546139, articleId=1325193105892311897, tenantId=1146029695717560320, journalId=1319690209941618849, language=CN, title=滩涂贝类采收设备自动驾驶滑移模型研究, columnId=0, journalTitle=智能化农业装备学报（中英文）, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对自走轮式滩涂贝类采收设备在作业时，由于滩涂底质受力应变导致车轮滑动产生相对位移，进而影响自动驾驶路径跟踪精度等问题，本研究提出了一种基于车辆运动学和动力学模型的滑移预测方法。根据四轮底盘运动学模型推导车辆运动轨迹，并结合轮胎横向和纵向受力特性，建立设备行走动力学模型，明确纵向滑移与横向滑移滑转的计算方法。本研究设计了一套自动驾驶试验平台，包括感知层、规划层和控制层，通过RTK-GNSS系统及相关传感器采集设备位置、转角及轮速等信息。通过纵向滑移试验与横向滑移滑转试验，探究采收设备在滩涂环境下的滑移特性。纵向滑移试验分析了电机PWM（pulse width modulation）与设备载重对滑移率的影响，并通过数据拟合建立纵向滑移模型。横向滑移滑转试验探讨了不同转向角条件下的滑移特性，拟合得出横向滑移滑转模型。为简化计算，模型采用泰勒多项式形式表达滑移系数函数，并通过试验验证模型准确性，实际轨迹与预测轨迹误差小于16%，显著降低了滩涂自动驾驶滑移干扰误差。本研究通过试验分析和模型优化，提升了滑移预测精度，为滩涂贝类采收设备在软质地面环境下实现高精度导航提供参考。未来可进一步研究滑移模型参数实时估计与触土部件动力学建模，以提高设备作业性能。

, authors=张志磊¹, 郭飞宏¹, 周炳洋¹, 徐斌¹, 吴昊¹, 母刚^{1, 2, 3, *, *}, authorsList=张志磊, 郭飞宏, 周炳洋, 徐斌, 吴昊, 母刚, authorCompany=null, correspAuthors=母刚, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=pDpQCPucCGqe08LXurh3xA==, magXml=rFv3k3oWKuUB+1rVhO4H/w==, pdfUrl=null, pdf=iX4WOlulXhIbDpswnuFfHw==, pdfFileSize=1309665, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=VThjpMXMUuy38MXrZlHaDQ==, mapNumber=null, fund=null)}, authors=[Author(id=1325193109340029798, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325193105892311897, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1455518698@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1325193109499413352, tenantId=1146029695717560320, journalId=1319690209941618849, articleId=1325193105892311897, authorId=1325193109340029798, language=EN, stringName=Zhilei ZHANG, firstName=Zhilei, middleName=null, lastName=ZHANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.School of Mechanical and Power Engineering, Dalian Ocean University, Dalian 116023, China, bio={"content":"

ZHANG Zhilei, E-mail: 1455518698@qq.com

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ZHANG Zhilei, E-mail: 1455518698@qq.com

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张志磊，男，1996年生，内蒙古通辽人，硕士研究生；研究方向为滩涂自动驾驶。E-mail：1455518698@qq.com

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张志磊，男，1996年生，内蒙古通辽人，硕士研究生；研究方向为滩涂自动驾驶。E-mail：1455518698@qq.com

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