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$^{\circ }$ in oocyte orientation control with maximum oocyte deformation of 2.70 $\mu$m throughout the orientation control process.

中文翻译：

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用于方向控制的可变形单元的机器人操作

可变形物体的机器人操作一直是机器人学中的经典话题。与橡皮球、衣服等合成可变形物体相比，生物细胞的变形能力强，更容易受到损伤。本文介绍了用于方向控制（平面外和平面内）的可变形细胞的机器人操作，这在临床（例如，体外受精）和生物医学（例如，克隆）应用中都是必需的。与基于经验的手动细胞定向控制相比，基于建模和路径规划的机器人方法有效地旋转细胞，同时始终保持最小的细胞变形以避免细胞损伤。建立力模型以确定微量移液器旋转球形或更一般的椭圆形卵母细胞所施加的最小力。通过接触力学模型将力信息转化为压痕，通过连接卵母细胞上的压痕位置形成微量移液器的操纵路径。最佳控制器旨在补偿卵母细胞机械性能的变化。通过对形状和大小差异具有鲁棒性的深度神经网络检测卵母细胞的极体。在实验中，该系统在极体检测中达到了 97.6% 的准确率，在卵母细胞定向控制中达到了 0.7$^{\circ }$，在整个定向控制过程中最大的卵母细胞变形为 2.70 $\mu$m。最佳控制器旨在补偿卵母细胞机械性能的变化。通过对形状和大小差异具有鲁棒性的深度神经网络检测卵母细胞的极体。在实验中，该系统在极体检测中达到了 97.6% 的准确率，在卵母细胞定向控制中达到了 0.7$^{\circ }$，在整个定向控制过程中最大的卵母细胞变形为 2.70 $\mu$m。最佳控制器旨在补偿卵母细胞机械性能的变化。通过对形状和大小差异具有鲁棒性的深度神经网络检测卵母细胞的极体。在实验中，该系统在极体检测中达到了 97.6% 的准确率，在卵母细胞定向控制中达到了 0.7$^{\circ }$，在整个定向控制过程中最大的卵母细胞变形为 2.70 $\mu$m。