Vision Based Picking System for Automatic Express Package Dispatching
This work addresses the labor-intensive task of package dispatching in logistics, but it is incremental as it builds on existing methods and is limited to specific scenarios.
The paper tackles the problem of automating express package dispatching by developing a vision-based robotic system that integrates RGB-D cameras, an improved grasp sampling algorithm, YOLO for package recognition, and a multi-modal hand, achieving successful handling of two typical express items in simulated practical conditions.
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.