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.

中文翻译：

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用于服装制造自动化的视觉引导自适应和优化机器人织物抓取系统


由于柔软片材的特性和所用面料的多样性，在服装制造中实现面料操作自动化仍然是一项具有挑战性的任务。本文介绍了一种自适应和优化的机器人织物处理系统，旨在应对这些挑战。该系统包括一个工业机器人、四个针式夹具和一个新型自适应夹具系统，能够自适应地调整夹具的位置，以适应服装面料部件的形状和材料特性。为此，对织物抓取特性进行了深入分析，考虑了材料特性、抓取位置和织物变形。根据分析的数据建立了一个预测织物挠度和折叠的两阶段机器学习模型。然后将该模型整合到视觉引导算法中，该算法使用相应的 CAD 数据确定服装部件上的最佳抓取点。此外，通过一种算法，将实时捕获的图像映射到基于 CAD 的形状信息，可以快速识别目标织物部件的准确位置。决策信息（即最佳抓取点和服装部件位置）随后被传输到机器人系统，用于自动化面料处理过程。对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面料裁剪和缝纫过程来完成服装制造自动化。