Automated Orientation Control of Motile Deformable Cells
Notice bibliographique
Résumé
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° 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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».