A vision-guided adaptive and optimized robotic fabric gripping system for garment manufacturing automation

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2024-09-16 DOI:10.1016/j.rcim.2024.102874

Young Woon Choi , Jiho Lee , Yongho Lee , Suhyun Lee , Wonyoung Jeong , Dae Young Lim , Sang Won Lee

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引用次数: 0

Abstract

Automating fabric manipulation in garment manufacturing remains a challenging task due to the characteristics of limp sheet materials and the diversity of fabrics used. This paper introduces an adaptive and optimized robotic fabric handling system, designed to address these challenges. The system comprises an industrial robot, four needle grippers, and a novel adaptive gripper jig system capable of adjusting the positions of the grippers adaptively to accommodate the shape and material properties of the garment fabric parts. To do this, an in-depth analysis of fabric gripping characteristics—accounting for material properties, gripping position, and fabric deformation—is conducted. A two-stage machine learning model predicting fabric deflection and folding is established from the analyzed data. This model is then incorporated into a vision-guided algorithm that determines the optimal gripping points on garment parts using corresponding CAD data. In addition, the exact position of the target fabric part is swiftly recognized via an algorithm that maps the real-time captured images to the CAD-based shape information. The decision-making information—namely optimal gripping points and garment part position—are subsequently transmitted to the robotic system for automated fabric handling process. The performance of the developed algorithms was quantitatively evaluated, and the integrated robotic system verified to be capable of completing garment manufacturing automation by connecting the processes of automatic fabric cutting and sewing.

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用于服装制造自动化的视觉引导自适应优化机器人织物抓取系统

由于软片材料的特性和所使用面料的多样性，服装制造中的面料自动操作仍是一项具有挑战性的任务。本文介绍了一种自适应优化机器人织物处理系统，旨在应对这些挑战。该系统由一个工业机器人、四个针式夹具和一个新颖的自适应夹具夹具系统组成，能够根据服装织物部件的形状和材料特性自适应地调整夹具的位置。为此，我们对织物抓取特性进行了深入分析，包括材料特性、抓取位置和织物变形。根据分析数据建立了一个预测织物变形和折叠的两阶段机器学习模型。然后将该模型纳入视觉引导算法，利用相应的 CAD 数据确定服装部件上的最佳抓取点。此外，通过将实时捕获的图像映射到基于 CAD 的形状信息的算法，可以迅速识别目标织物部件的准确位置。随后，将决策信息（即最佳抓取点和服装部件位置）传输给机器人系统，以实现自动织物处理过程。对所开发算法的性能进行了定量评估，并验证了集成机器人系统能够通过连接自动织物裁剪和缝纫流程来完成服装制造自动化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.