Micro-Domain Force Estimation Using Hall-Effect Sensors for a Magnetic Microrobotic Station
Bibliographic record
Abstract
This paper introduces a novel micro-domain force estimation method for applications in a magnetic-haptic micromanipulation platform (MHMP). The MHMP employs the magnetic levitation technology in micro-domain worlds for ultra-high precision micromanipulation. In the MHMP, a microrobot that consists of a magnetic head and a body that includes electronic parts and an end-effector is manipulated by regulating an external magnetic field. The MHMP has been equipped with a haptic technology to allow a human operator to feel micro-domain environments and to intervene in dexterous tasks due to the poor knowledge from micro-worlds. To preserve a high feeling of a micro-domain environment for a human operator, the applied force/torque from the environment to the microrobot are required to be directly measured by specific sensors. Due to the size restriction, attaching force sensors to our microrobot is impractical. Therefore, we use a combination of Hall-effect sensor in the structure of the MHMP to estimate a single-axis force, eliminating the need for sensors on the microrobot. The Hall-sensors measure the magnetic flux and determine the location of the horizontally zero magnetic field gradient, Bmax location. It was realized that the applied force from the environment to the microrobot is linearly proportional to the distance of the microrobot from the Bmax location. The magnetic force which is equal to the environment force is calibrated using a cantilever deflection. The developed micro-domain force estimation method is verified experimentally, and it was demonstrated that this method has promising potential in estimating the environmental force applied to the microrobot in a non-contact way.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".