用视觉和深度学习实现工业场景下透明塑料袋的自动抓取与切割。
Vision-based Manipulation of Transparent Plastic Bags in Industrial Setups
- 基于CNN识别复杂光照下的透明袋子，结合深度感知实现3D定位。
- 通过真空夹持与柔顺控制，实现在动态环境中的安全稳定抓取。
- 在FRANKA机械臂上验证成功，适用于8层批量装载设备自动化。
本文针对工业场景中透明塑料袋的自主切割与开包任务，解决视觉引导操作面临的挑战，契合工业4.0理念。工业4.0依托数据、连接性、分析与机器人技术，提升价值链的可及性与可持续性。协作机器人（cobots）的集成对提高效率与安全性至关重要。所提方案采用先进的机器学习算法，特别是卷积神经网络（CNN），在不同光照与背景条件下识别透明塑料袋。结合跟踪算法与深度传感技术，实现抓取与放置过程中的三维空间感知。系统应对了抓取点选择、真空夹持技术下的柔顺控制以及动态环境中的实时自动化交互等关键问题。在实验室环境中，使用FRANKA机械臂完成了系统测试与验证，证明其在满足特定需求并经过严格测试后，具备广泛应用于工业场景的潜力，尤其在基于8层批量装载器的透明塑料袋开包与切割任务中表现高效。
原文摘要 · Abstract (English)
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.
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