Robotic Manipulation of Sperm As a Deformable Linear Object

The robotic manipulation of deformable linear ob- jects 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 recapitu- lated 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◦, a tail curvedness of 0.14 μm−1, and an operation time of 5.6 s, all significantly less than those of the manual approach. The Manuscript received 20 January 2022; accepted 5 March 2022. Date of publication 23 March 2022; date of current version 4 October 2022. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada and in part by the Canada Research Chairs Program. This article was recommended for publication by Associate Editor L. Zhang and Editor A. Menciassi upon evaluation of the reviewers’ comments. (Corresponding author: Yu Sun.) Changsheng Dai, Guanqiao Shan, Hang Liu, and Yu Sun are with the De- partment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (e-mail: changsheng.dai@mail.utoronto.ca; gq.shan@mail.utoronto.ca; drhang.liu@mail.utoronto.ca; sun@mie.utoronto. ca). Changhai Ru is with the School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China (e-mail: rchhai@163.com). This article has supplementary downloadable material available at https://ieeexplore.ieee.org, provided by the authors. The material consists of a video, viewable with Windows Media Player, showing robotic manipulation of sperm as a deformable linear object. The video shows that human sperm was robotically rotated to the target orientation using a standard clinical setup of a glass micropipette. To deal with large variance of sperm tails in shape and dimension, deep neural networks were developed for robust tail detection. With the intrinsic challenges in cell manipulation, such as micromanipulator’s limited degrees of freedom and cell’s varied mechanical parameters, math- ematical modeling and path planning strategies were developed to rotate a sperm to the target orientation. A state transition function was formulated to update the manipulation point based on sperm tail’s deformation behavior. A model-predictive controller was designed to minimize the orientation error and manipulation path length. This article has supplementary material provided by the au- thors and color versions of one or more figures available at https://doi.org/10.1109/TRO.2022.3158200. Digital Object Identifier 10.1109/TRO.2022.3158200 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.