| Title: |
Pepper to fall: a perception method for sweet pepper robotic harvesting. |
| Authors: |
Polic, Marsela, Tabak, Jelena, Orsag, Matko |
| Source: |
Intelligent Service Robotics; Apr2022, Vol. 15 Issue 2, p193-201, 9p |
| Abstract: |
In this paper we propose a robotic system for picking peppers in a structured robotic greenhouse environment. A commercially available robotic manipulator is equipped with an RGB-D camera used to detect a correct pose to grasp peppers. The detection algorithm uses the state-of-the-art pretrained CNN architecture. The system was trained using transfer learning on a synthetic dataset made with a 3D modeling software, Blender. Point cloud data are used to detect the pepper's 6DOF pose through geometric model fitting, which is used to plan the manipulator motion. On top of that, a state machine is derived to control the system workflow. We report the results of a series of experiments conducted to test the precision and the robustness of detection, as well as the success rate of the harvesting procedure. [ABSTRACT FROM AUTHOR] |
|
Copyright of Intelligent Service Robotics is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |
