This paper proposes 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 pre-trained 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 the 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.
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Polić, Marsela & Tabak, Jelena & Orsag, Matko. (2021). Pepper to fall: a perception method for sweet pepper robotic harvesting. Intelligent Service Robotics. 10.1007/s11370-021-00401-7.
