# Vision Based Picking System for Automatic Express Package Dispatching

## TL;DR

This paper introduces a vision-based robotic system that automates express package dispatching using RGB-D cameras, deep learning for recognition, and a multi-modal gripper, demonstrating effective handling of real-world scenarios.

## Contribution

The paper presents an integrated robotic system combining advanced grasp sampling, deep learning recognition, and multi-modal gripping for automated package dispatching.

## Key findings

- System successfully automates package dispatching tasks.
- High accuracy in package recognition and grasping.
- Effective handling of overlapped and deformable objects.

## Abstract

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.