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Robotic Manipulation of Sperm as a Deformable Linear Object.

Authors :
Dai, Changsheng
Shan, Guanqiao
Liu, Hang
Ru, Changhai
Sun, Yu
Source :
IEEE Transactions on Robotics. Oct2022, Vol. 38 Issue 5, p2799-2811. 13p.
Publication Year :
2022

Abstract

The robotic manipulation of deformable linear objects is a classic and challenging topic. Apart from synthetic objects, such as wires and cables, linear objects are also commonly found in biological cells and organisms. Biomanipulation of such objects is hampered by difficulties, such as limited degrees of freedom of micromanipulators and varied mechanical properties of the biological entities to manipulate. This article presents a robotic manipulation of human sperm, which are deformable cells with a linear shape. The shape and movement of the cell are recapitulated by our developed geometric and kinematic models. Under unfixed constraints between the end-effector and the cell, path planning is designed to update the manipulation point to control cell deformation. A state transition function is formulated in path planning to handle the stiffness variations of sperm without force sensing. A model-predictive controller is designed to minimize the orientation error and manipulation path length. To detect sperm tail for visual feedback, an accuracy of 98% was achieved via deep neural networks. The robotic manipulation of human sperm was performed using a standard clinical setup of a glass micropipette to rotate a sperm to the target orientation. Experimental results showed that robotic sperm rotation achieved an orientation error of 0.8 $^{\circ }$ , a tail curvedness of 0.14 $\mu$ m $^{-1}$ , and an operation time of 5.6 s, all significantly less than those of the manual approach. The less orientation error and tail curvedness after robotic rotation led to a significantly lower speed of sperm entering the micropipette during sperm aspiration, resulting in a higher success rate of 97% (versus 76% after manual rotation) for aspiration control. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15523098
Volume :
38
Issue :
5
Database :
Academic Search Index
Journal :
IEEE Transactions on Robotics
Publication Type :
Academic Journal
Accession number :
160689038
Full Text :
https://doi.org/10.1109/TRO.2022.3158200