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Vision Based Picking System for Automatic Express Package Dispatching

机译:基于视觉的自动快递包裹分拣系统

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This paper presents a vision based robotic system to handle the picking problem involved in automatic express package dispatching. By utilizing two RealSense RGB-D cameras and one UR10 industrial robot, package dispatching task which is usually done by human can be completed automatically. In order to determine grasp point for overlapped deformable objects, we improved the sampling algorithm proposed by the group in Berkeley to directly generate grasp candidate from depth images. For the purpose of package recognition, the deep network framework YOLO is integrated. We also designed a multi-modal robot hand composed of a two-fingered gripper and a vacuum suction cup to deal with different kinds of packages. All the technologies have been integrated in a work cell which simulates the practical conditions of an express package dispatching scenario. The proposed system is verified by experiments conducted for two typical express items.
机译:本文提出了一种基于视觉的机器人系统来处理自动包裹快递中的拣选问题。通过利用两台RealSense RGB-D摄像机和一台UR10工业机器人,可以自动完成通常由人完成的包裹分派任务。为了确定重叠的可变形物体的抓握点,我们改进了伯克利小组提出的采样算法,以直接从深度图像中生成抓握候选物。出于程序包识别的目的,集成了深度网络框架YOLO。我们还设计了一种多模式机器人手,该机器人手由两个手指的抓手和一个真空吸盘组成,可以处理不同种类的包装。所有技术都已集成在一个工作单元中,该工作单元模拟快递包裹派遣场景的实际条件。通过对两个典型快递项目进行的实验验证了所提出的系统。

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