Giang Truong Thi Huong, Ryoo Young-Jae
Department of Information Technology, Tay Nguyen University, Buonmathuot 63161, Vietnam.
Department of Electrical and Control Engineering, Mokpo National University, Muan-gun 58554, Republic of Korea.
Biomimetics (Basel). 2024 Mar 5;9(3):161. doi: 10.3390/biomimetics9030161.
This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the articulated manipulator. A semantic segmentation neural network is employed to recognize the different parts of the sweet pepper plant, which is then used to create 3D point clouds for detecting the pruning position and the manipulator pose. Eventually, a manipulator robot is controlled to prune the crop part. This article provides a detailed description of the three tasks involved in building the sweet pepper pruning system and how to integrate them. In the experiments, we used a robot arm to manipulate the pruning leaf actions within a certain height range and a depth camera to obtain 3D point clouds. The control program was developed in different modules using various programming languages running on the ROS (Robot Operating System).
本文提出了一种自主机器人系统，该系统利用深度学习语义分割和关节式机械手对甜椒叶片进行修剪。该系统涉及三项主要任务：作物部位感知、修剪位置检测以及关节式机械手控制。采用语义分割神经网络识别甜椒植株的不同部位，然后利用该网络创建三维点云，以检测修剪位置和机械手姿态。最终，控制机械手机器人对作物部位进行修剪。本文详细描述了构建甜椒修剪系统所涉及的三项任务以及如何将它们集成。在实验中，我们使用机器人手臂在一定高度范围内操纵修剪叶片的动作，并使用深度相机获取三维点云。控制程序是使用运行在ROS（机器人操作系统）上的各种编程语言在不同模块中开发的。
