Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/RCAR47638.2019.9044094 | arXiv: Robotics |
DocType | Volume | Citations |
Journal | abs/1902.08951 | 0 |
PageRank | References | Authors |
0.34 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengfan Wang | 1 | 0 | 1.01 |
| Xin Jiang | 2 | 150 | 32.43 |
| Jie Zhao | 3 | 0 | 0.34 |
| Xiaoman Wang | 4 | 0 | 1.35 |
| Weiguo Zhou | 5 | 0 | 1.69 |
| Liu YH | 6 | 1540 | 185.05 |