Six-degree-of-freedom magnetic actuation for wireless microrobotics
Bibliographic record
Abstract
Existing remotely actuated magnetic microrobots exhibit a maximum of only five-degree-of-freedom (DOF) actuation, as creation of a driving torque about the microrobot magnetization axis is not achievable. This lack of full orientation control limits the effectiveness of existing microrobots for precision tasks of object manipulation and orientation for advanced medical, biological and micromanufacturing applications. This paper presents a magnetic actuation method that allows remotely powered microrobots to achieve full six-DOF actuation by considering the case of a non-uniform magnetization profile within the microrobot body. This non-uniform magnetization allows for additional rigid-body torques to be induced from magnetic forces via a moment arm. A general analytical model presents the working principle for continuous and discrete magnetization profiles, which is applied to permanent or non-permanent (soft) magnet bodies. Several discrete-magnetization designs are also presented which possess reduced coupling between magnetic forces and induced rigid-body torques. Design guidelines are introduced which can be followed to ensure that a magnetic microrobot design is capable of six-DOF actuation. A simple permanent-magnet prototype is fabricated and used to quantitatively demonstrate the accuracy of the analytical model in a constrained-DOF environment and qualitatively for free motion in a viscous liquid three-dimensional environment. Results show that desired forces and torques can be created with high precision and limited parasitic actuation, allowing for full six-DOF actuation using limited feedback control.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".