A Novel Magnetic Transmission for Powerful Miniature Surgical Robots
Bibliographic record
Abstract
Untethered magnetically actuated robots present significant advantages for biomedical applications by allowing power to be delivered wirelessly to remote, miniature robots through flesh and bone. Nevertheless, generating enough usable force to interact with their environment remains a challenge: Most millimeter-size magnetic robots achieve forces of much less than 0.1 N, which limits their medical applications. Therefore, a compact, millimeter-scale transmission is needed to amplify the output force. A novel microtransmission system is presented, composed of a driving magnet suspended between two twisted string actuators. An analytical model of the transmission is formulated to predict its behavior under both fixed load and fixed displacement configurations. Experimental measurements are used to validate the predicted maximum achievable force, to quantify the transmission performance over numerous cycles, to determine the effect of different string materials, and to determine the impact of hysteresis and viscoelasticity. Finally, the transmission is integrated into a 3 mm diameter surgical gripper prototype to demonstrate its effectiveness. With only a modest 20 mT of applied magnetic field, the gripper generates 1.09 N gripping force—a factor of 35 improvement in mechanical advantage and a factor of 62 improvement in gripping force compared to a similar size gripper using direct magnetic actuation. This microtransmission for miniature magnetic robots will enable high-strength untethered robots for minimally invasive surgical applications.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".