夹持器
稳健性(进化)
接触力
控制器(灌溉)
触觉传感器
非线性系统
软机器人
工程类
计算机科学
控制理论(社会学)
线性
控制工程
人工智能
执行机构
机器人
控制(管理)
机械工程
电子工程
物理
基因
农学
生物
化学
量子力学
生物化学
作者
Hui Yang,Jiaqi Liu,Wenbo Liu,Weirui Liu,Zilong Deng,Yunzhi Ling,Changan Wang,Meixia Wu,Lihui Wang,Li Wen
出处
期刊:Soft robotics
[Mary Ann Liebert]
日期:2024-04-01
卷期号:11 (2): 230-243
被引量:5
标识
DOI:10.1089/soro.2022.0221
摘要
Soft grippers with good passive compliance can effectively adapt to the shape of a target object and have better safe grasping performance than rigid grippers. However, for soft or fragile objects, passive compliance is insufficient to prevent grippers from crushing the target. Thus, to complete nondestructive grasping tasks, precision force sensing and control are immensely important for soft grippers. In this article, we proposed an online learning self-tuning nonlinearity impedance controller for a tactile self-sensing two-finger soft gripper so that its grasping force can be controlled accurately. For the soft gripper, its grasping force is sensed by a liquid lens-based optical tactile sensing unit that contains a self-sensing fingertip and a liquid lens module and has many advantages of a rapid response time (about 0.04 s), stable output, good sensitivity (>0.4985 V/N), resolution (0.03 N), linearity (R2 > 0.96), and low cost (power consumption: 5 mW, preparation cost
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