Automated Orientation Control of Motile Deformable Cells

Automated manipulation of deformable objects is challenging due to the object's deformation behavior. Different from still deformable objects such as wires and cloth, biological organisms such as sperm and worms are both deformable and motile, requiring the control of both deformation and motion. This paper reports automated orientation control of live sperm, as an example of motile deformable cells. Robotic manipulation of human sperm was performed by using a glass micropipette, which is a standard clinical tool, to rotate individual motile sperm. Sperm rotation must be performed before immobilization, as required in clinical cell surgery for infertility treatment. To control tail deformation during sperm rotation, a path planner was designed based on kinematic analysis and manipulation point update. To deal with the intrinsic motion of' a motile sperm, a motorized stage was controlled to compensate for sperm swimming motion, and an observer was designed to decouple sperm orientation from its wiggling motion. A sliding mode controller was designed to cope with stiffness variances along the sperm tail and among different sperm. Deep neural networks were developed for robust sperm tail detection, and Kalman filter was used to predict tail motion. Experimental results demonstrated that automated sperm manipulation achieved an orientation error of 0.8 degrees and operation time of 6.8 s, both significantly less than those of manual operation. The designed observer was effective to reduce sperm orientation error by reducing the disturbance from sperm wiggling motion. The developed sliding mode controller outperformed the PID controller in operation time, reducing the time of oocyte exposure to the ambient environment. Note to Practitioners-This work tackled the challenge of rotating a fast-swimming and deformable sperm in clinical cell surgeries. Automated manipulation of deformable objects has wide applications in industrial and service settings such as manipulating wires and folding cloth. However, the intrinsic motion of a motile sperm and the lack of a rotational degree of freedom in standard micromanipulators pose difficulties to automated sperm manipulation. In this paper, we propose automation techniques for sperm orientation control. For sperm tail detection, deep learning was used to handle the variances of shape and length among different sperm. A path planning strategy and a controller were designed to achieve automated rotation of' motile sperm, with its deformation and motion both controlled. The developed methods can be generalized to the manipulation of other deformable objects such as wires, cables and cloth. These objects exhibit significant variance of mechanical properties, and calibration is often time-consuming. The designed controller can be used to manipulate deformable objects with robustness to varied mechanical parameters. Path planning was designed by updating the manipulation point based on the object's deformation behavior, and is suitable in manipulation where constraints are imposed such as the object's strain.