弯曲
接触力
有限元法
正确性
计算机科学
模拟
机器人
过程(计算)
约束(计算机辅助设计)
变形(气象学)
结构工程
工程类
人工智能
机械工程
声学
材料科学
算法
物理
量子力学
操作系统
复合材料
作者
Zhen Zhang,Jun Zhou,Boyang Yi,Baohua Zhang,Kai Wang
标识
DOI:10.1016/j.compag.2022.107489
摘要
Securely harvesting apples without damage remains a challenge owing to their softness, vulnerability, and irregular shapes. In this study, a flexible swallowing (FS) gripper was designed and tested for harvesting apples. To analyze the adaptive grasping process of this gripper, first, a closed equation of the force-to-deformation model for the finger was proposed based on the Chained-Beam-Constraint Model (CBCM). The correctness of the model was verified by finite element analysis (FEA), and the maximum error was no more than 1.7 %. Subsequently, the grasping force sensing model related to bending angle of the finger was established based on the force-to-deformation model. Finally, taking an apple with a diameter of 80 mm as the experimental object, several groups of grasping tests were conducted with the gripper. The bending angle and force sensing error for different grasping positions of the fingers were analyzed and compared. The results demonstrated that the gripper can sufficiently perceive the grasping force, and the force sensing accuracy in the middle of the finger was the best, with an average absolute error and relative error of 0.153 N and 5.65 %, respectively; A better envelope ability and greater grasping force was obtained when grasping with the bottom of the finger; the maximum bending angle and maximum contact force were 22.6° and 5.72 N respectively. Moreover, a harvesting experiment using this gripper installed at the end of a robot arm was conducted, which further verified that this gripper has a good grasping ability for apples and can be sufficiently applied in actual robot harvesting.
科研通智能强力驱动
Strongly Powered by AbleSci AI