Low-Profile 6-Axis Differential Magnetic Force/Torque Sensing
Bibliographic record
Abstract
Force/torque sensing on hand-held tools enables control of applied forces, which is often essential in both tele-robotics and remote guidance of people. However, existing force sensors are either bulky, complex, or have insufficient load rating. This paper presents a novel 6 axis force-torque sensor based on differential magnetic field readings in a collection of low-profile sensor modules placed around a tool or device. The instrumentation is easy to install but nonetheless achieves good performance. A detailed mathematical model and optimization-based design procedure are also introduced. The modeling, simulation, and optimization of the force sensor are described and then used in the electrical and mechanical design and integration of the sensor into an ultrasound probe. Through a neural network-based nonlinear calibration, the sensor achieves average root-mean-square test errors of 0.41 N and 0.027 Nm compared to an off-the-shelf ATI Nano25 sensor, which are 0.80% and 1.16% of the full-scale range respectively. The sensor has an average noise power spectral density of less than 0.0001 N/√Hz, and a 95% confidence interval resolution of 0.0086 N and 0.063 Nmm. The practical readout rate is 1.3 kHz over USB serial and it can also operate over Bluetooth or Wi-Fi. This sensor can enable instrumentation of manual tools to improve the performance and transparency of teleoperated or autonomous systems.
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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) | 0.000 | 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) | 0.002 | 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".