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Octopus-Inspired Microgripper for Deformation-Controlled Biological Sample Manipulation.

Authors :
Qian, Cheng
Tong, Mingsi
Yu, Xinghu
Zhuang, Songlin
Gao, Huijun
Source :
IEEE Transactions on Neural Networks & Learning Systems; May2022, Vol. 33 Issue 5, p1857-1866, 10p
Publication Year :
2022

Abstract

Predators in nature grip their prey in different ways, which give innovational ideas of gripping approaches in industrial applications. Octopus performs flexible gripping with the help of vacuum grippers, suction cups, which inspired a new type of microgripper for biological sample micromanipulation. The proposed gripper consists of a glass pipette and a pump driven by a step-motor. The step-motor is controlled with adaptive robust control to adjust the gripping pressure applied on the biological sample. A dynamic model is developed for the biological sample aiming for better deformation control performance. A visual detection algorithm is developed for data processing to identify the parameters in the dynamic model and the detection result of visual algorithm is also used as feedback of adaptive robust control, which diminishes the negative influence of parameter and model uncertainties. Zebrafish larva was used as the testing sample for experiment and the corresponding parameters were identified experimentally. The experimental results correlated well with the model predicted deformation curve and visual detection algorithm provided promising accuracy, which is less than $4~\boldsymbol{\mu }\text{m}$. Adaptive robust control provides fast and accuracy response in point-to-point deformation testing, and the average responding time is less than 30 s and the average error is no larger than 1 pixel. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2162237X
Volume :
33
Issue :
5
Database :
Complementary Index
Journal :
IEEE Transactions on Neural Networks & Learning Systems
Publication Type :
Periodical
Accession number :
156718274
Full Text :
https://doi.org/10.1109/TNNLS.2021.3070631