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织物的多样性，服装制造中的自动化织物处理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，旨在解决这些挑战。该系统包括一个工业机器人、四个针夹和一个新颖的自适应夹钳夹具系统，该系统能够自适应地调整夹钳的位置以适应服装面料部分的形状和材料特性。为此，需要对织物抓握特性进行深入分析，考虑材料特性、抓握位置和织物变形。根据分析的数据建立了预测织物偏转和折叠的两阶段机器学习模型。然后将该模型合并到视觉引导算法中，该算法使用相应的 CAD 数据确定服装部件上的最佳抓握点。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，可以快速识别目标织物部分的确切位置。决策信息（即最佳抓握点和服装部件位置）随后被传输到机器人系统，以实现自动化织物处理过程。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面料裁剪和缝纫过程来完成服装制造自动化。