Designing Multi-Axis Force–Torque Sensors by Minimizing the Amplitudes of Their Nonlinear Displacements
Bibliographic record
Abstract
Compliant multi-axis force–torque sensors play a crucial role in many emerging robotic applications, such as telemanipulation, haptic devices and human-robot physical interaction. In order to synthesize the compliant architectures at the core of these sensors, several researchers have devised performance indices from mechanism theory. This paper follows the same approach, but includes the innovation of using the changes in the compliant mechanism geometry as a new performance index. Once external forces are applied, the compliant mechanism deviates from its unloaded configuration, and thus, changes in geometry prevent the sensor from exhibiting a linear response. In order to minimize this nonlinear behavior, the potential sources of error are analyzed by applying linear algebra techniques to the expression of the Cartesian force mapping. Two performance indices are then presented and combined. The first index measures the variations of the Jacobian matrix about the unloaded configuration. The second index measures the amplification of the error arising from the joint displacements measurement. The resulting indices can be expressed symbolically, making them easier to evaluate and synthesize. Finally, we apply the performance indices we have developed to simple compliant mechanisms, and discuss the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".