人工智能
方向(向量空间)
卵母细胞
机器人学
计算机科学
移液管
变形
计算机视觉
几何学
机器人
数学
生物
化学
细胞生物学
物理化学
胚胎
作者
Changsheng Dai,Zhuoran Zhang,Yuchen Lu,Guanqiao Shan,Xian Wang,Qili Zhao,Changhai Ru,Yu Sun
出处
期刊:IEEE Transactions on Robotics
[Institute of Electrical and Electronics Engineers]
日期:2019-10-28
卷期号:36 (1): 271-283
被引量:61
标识
DOI:10.1109/tro.2019.2946746
摘要
Robotic manipulation of deformable objects has been a classic topic in robotics. Compared to synthetic deformable objects such as rubber balls and clothes, biological cells are highly deformable and more prone to damage. This article presents robotic manipulation of deformable cells for orientation control (both out-of-plane and in-plane), which is required in both clinical (e.g., in vitro fertilization) and biomedical (e.g., clone) applications. Compared to manual cell orientation control based on empirical experience, the robotic approach, based on modeling and path planning, effectively rotates a cell, while consistently maintaining minimal cell deformation to avoid cell damage. A force model is established to determine the minimal force applied by the micropipette to rotate a spherical or, more generally, ellipsoidal oocyte. The force information is translated into indentation through a contact mechanics model, and the manipulation path of the micropipette is formed by connecting the indentation positions on the oocyte. An optimal controller is designed to compensate for the variations of mechanical properties across oocytes. The polar body of an oocyte is detected by deep neural networks with robustness to shape and size differences. In experiments, the system achieved an accuracy of 97.6% in polar body detection and an accuracy of 0.7° in oocyte orientation control with maximum oocyte deformation of 2.70 μm throughout the orientation control process.
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