Bibliographic Details
| 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] |
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| Database: |
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