夹持器
人工智能
任务(项目管理)
软机器人
刚度
计算机科学
机械臂
机器人
集合(抽象数据类型)
计算机视觉
模拟
工程类
控制工程
机械工程
结构工程
程序设计语言
系统工程
作者
Francesco Visentin,Fabio Castellini,Riccardo Muradore
标识
DOI:10.1016/j.compag.2023.108202
摘要
Harvesting fruits and vegetables is a complex task worth to be fully automated with robotic systems. It involves several precision tasks that have to be performed with accuracy and the appropriate amount of force. Classical mechanical grippers, due to the complex control and stiffness, cannot always be used to harvest fruits and vegetables. Instead, the use of soft materials could provide a visible advancement. In this work, we propose a soft, sensorized gripper for harvesting applications. The sensing is performed by tracking a set of markers integrated into the soft part of the gripper. Different machine learning-based approaches have been used to map the markers’ position and dimensions into forces in order to perform a close-loop control of the gripper. Results show that force can be measured with an error of 2.6% in a range from 0 to 4 N. The gripper was integrated into a robotic arm having an external vision system used to detect plants and fruits (strawberries in our case scenario). As a proof of concept, we evaluated the performance of the robotic system in a laboratory scenario. Plant and fruit identification reached a positive rate of 98.2% and 92.4%, respectively, while the correct picking of the fruits, by removing it from the stalk without a direct cut, achieved an 82% of successful rate.
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