Name
Abstract | ||
|---|---|---|
Nowadays, robots are heavily used in factories for different tasks, most of them including grasping and manipulation of generic objects in unstructured scenarios. In order to better mimic a human operator involved in a grasping action, where he/she needs to identify the object and detect an optimal grasp by means of visual information, a widely adopted sensing solution is Artificial Vision. Nonetheless, state-of-art applications need long training and fine-tuning for manually build the object’s model that is used at run-time during the normal operations, which reduce the overall operational throughput of the robotic system. To overcome such limits, the paper presents a framework based on Deep Convolutional Neural Networks (DCNN) to predict both single and multiple grasp poses for multiple objects all at once, using a single RGB image as input. Thanks to a novel loss function, our framework is trained in an end-to-end fashion and matches state-of-art accuracy with a substantially smaller architecture, which gives unprecedented real-time performances during experimental tests, and makes the application reliable for working on real robots. The system has been implemented using the ROS framework and tested on a Baxter collaborative robot. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.aei.2020.101052 | Advanced Engineering Informatics |
Keywords | DocType | Volume |
Collaborative robotics,Deep learning,Vision-guided robotic grasping,Industry 4.0 | Journal | 44 |
ISSN | Citations | PageRank |
1474-0346 | 1 | 0.41 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Bergamini | 1 | 4 | 2.15 |
| Mario Sposato | 2 | 1 | 0.41 |
| marcello pellicciari | 3 | 23 | 6.03 |
| Margherita Peruzzini | 4 | 33 | 10.93 |
| Simone Calderara | 5 | 936 | 54.25 |
| Juliana Schmidt | 6 | 1 | 0.41 |