Untethered Miniature Soft Robots: Modeling and Design of a Millimeter-Scale Swimming Magnetic Sheet
Bibliographic record
Abstract
The actuation and control of miniature soft robots are challenging problems due to their limited onboard space and flexible bodies. Smart magnetic materials are promising candidates to address these challenges since they can be powered and guided remotely by magnetic field for functionalities, such as swimming, grasping, and pumping. In this study, we program an undulatory swimming gait into a small rectangular sheet that is made of a flexible magnetic homogeneous composite. The sheet bears a sinusoidal magnetization profile throughout its body and deforms into undulatory shapes in a rotating uniform magnetic field that aligns with its length. The traveling wave-like deformation of the sheet interacts with the surrounding liquid and propels the sheet in a bidirectional nonholonomic swimming gait. Previous studies on this sheet were not able to model the deformation accurately or characterize the swimming systematically due to a lack of understanding of the underlying physical principles involved. For the first time, we develop a model from underlying physical principles to explain and predict the sheet deformation, which enables it to swim at air-water interfaces and generate propulsive forces under water with an additional stiff frame. The swimming capability and maneuverability of the millimeter-scale sheet are demonstrated in experiments, and its swimming performances in various scenarios are characterized quantitatively. The soft swimming sheet can potentially be used for microrobotic tasks, such as delivering cargo or transporting individual cells in poorly accessible workspaces.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".