工业场景中透明塑料袋的视觉操作
Vision-based Manipulation of Transparent Plastic Bags in Industrial Setups
AI总结：
本文提出一种基于卷积神经网络和深度感知的视觉操作系统，结合真空抓取与柔顺控制，在FRANKA机器人上实现了工业场景中透明塑料袋的自主识别、拆包与切割。
摘要：
本文针对工业场景中透明塑料袋自主切割与拆包所面临的视觉操作挑战展开研究，并与工业4.0范式保持一致。工业4.0以数据、连接、分析和机器人技术为驱动，有望在整个价值链中提升可及性与可持续性。将包括协作机器人（cobots）在内的自主系统集成到工业流程中，对于提高效率和安全性至关重要。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），在不同光照和背景条件下识别透明塑料袋。利用跟踪算法和深度感知技术实现拾取与放置过程中的三维空间感知。该系统解决了抓取与操作中的挑战，考虑了最佳抓取点、基于真空抓取技术的柔顺控制，以及用于在动态环境中安全交互的实时自动化。该系统在实验室中使用FRANKA机器人手臂成功完成测试与验证，展示了其广泛工业应用的潜力，同时证明了在特定要求和严格测试基础上，为8层堆叠散装装载机实现透明塑料袋自动拆包与切割的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
