This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability throughout the value chain. The integration of autonomous systems, including collaborative robots (cobots), into industrial processes is pivotal for efficiency and safety. The proposed solution employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), to identify transparent plastic bags under varying lighting and background conditions. Tracking algorithms and depth sensing technologies are utilized for 3D spatial awareness during pick and placement. The system addresses challenges in grasping and manipulation, considering optimal points, compliance control with vacuum gripping technology, and real-time automation for safe interaction in dynamic environments. The system's successful testing and validation in the lab with the FRANKA robot arm, showcases its potential for widespread industrial applications, while demonstrating effectiveness in automating the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader based on specific requirements and rigorous testing.

翻译：本文针对工业场景中透明塑料袋的自主切割与拆包所面临的视觉操控挑战展开研究，符合工业4.0范式。由数据、互联性、分析与机器人技术驱动的工业4.0，有望在整个价值链中提升可及性与可持续性。将自主系统（包括协作机器人）集成到工业流程中，对提升效率与保障安全至关重要。所提出的解决方案采用先进的机器学习算法，特别是卷积神经网络（CNNs），以在不同光照与背景条件下识别透明塑料袋。系统利用跟踪算法与深度感知技术实现拾取放置过程中的三维空间感知。该系统通过考虑最优抓取点、采用真空夹持技术的柔顺控制以及动态环境中安全交互的实时自动化，解决了抓取与操控中的难题。在实验室中使用FRANKA机械臂进行的成功测试与验证，展示了该系统广泛的工业应用潜力，同时证明了其基于特定需求与严格测试，在自动化拆解和切割8层堆垛式散装装载机中透明塑料袋方面的有效性。