Ultra Low-Noise FPGA-Based Six-Axis Optical Force–Torque Sensor: Hardware and Software
Bibliographic record
Abstract
In this article, we present the novel hardware and software architecture of a smart optical force–torque sensor. The proposed configurable, modular, and compact electronics lead to performance characteristics that cannot be reached by currently available sensors: ultra-low noise with average noise power spectral density of 15 nV/$\sqrt{\text{Hz}}$over a signal bandwidth of 500 Hz, a resolution of 0.0001% full scale at a 95% confidence level, and a hardware latency of less than 100$\mu$s. Performance is achieved by local synchronized oversampling of the sensor's optical transducers and parallel hardware processing of the sensor data using a field-programmable gate array (FPGA). The FPGA's reconfigurability provides for easy customization and updates; for example, by increasing the FPGA system clock rate to a maximum of 160 MHz, latency can be decreased to 50$\mu$s, limited by the current analog-to-digital converter. Furthermore, the approach is generic and could be duplicated with other types of transducers. An inertial measurement unit and a temperature sensor are integrated into the sensor electronics for gravity, inertia, and temperature compensations. Two software development kits that allow for the use of the sensor and its integration into the robot operating system have been developed and are discussed.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".