Reliable Grasping of Three-Dimensional Untethered Mobile Magnetic Microgripper for Autonomous Pick-and-Place
Bibliographic record
Abstract
Manipulation of micrometer to millimeter-scale objects is central to biotechnological and medical applications involving small-scale robotic devices. Mobile untethered microgrippers have been developed, which use magnetic fields for motion and activation of grasping. This letter extends the capabilities of such microgrippers by presenting the first example of reliable and autonomous three-dimensional (3-D) micrograsping and cargo delivery of a microgripper using simple control strategies. This endeavor will allow microgrippers to reliably grasp and transfer microobjects, such as cells, with minimal user input, which is ideal for cooperative tasks performed by multiple microgrippers in future work. The proposed controller autonomously manipulates a 3-D magnetic microgripper for pick-and-place tasks in the 3-D space. By regulating the remotely applied force on the microgripper, the 3-D position and velocity of the microgripper are controlled, and the microgripper grasping, i.e., opening and closing, is determined by the magnetic field strength. In experiments, the microgripper successfully grasps cargoes with cubic, irregular, triangular, and beam shapes using at most 5, 8 , 20, and 24 attempts, respectively. The microgripper shown here demonstrates fast grasping due to their complete magnetic actuation method. Moreover, a preliminary cell viability test suggests that the microgripper has no adverse effects on living cells. This study proves the proposed microgripper and controller to be agile and reliable tools for biomedical tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".