Bibliographic record
Abstract
Motivated by the need for accurate joint torque sensing in robots, we designed a new torque sensor, based on the hollow hexaform geometry. Its key features were its extremely high sti ness and its insensitivity to the set of support forces and moments which persist in a robot joint. These features permit to mount the sensor directly in the joints of a robot manipulator leading to accurate joint torque sensing and to a compact and modular design. The structure of the sensor also exhibits strain concentration to torsion loads which maximizes the sensitivity to torsion without sacrificing torsional sti ness. Other design issues such as practical shape consideration, material properties and overloading also considered. The sensor geometry was analyzed and optimized using the finite element method. The sensor was tested extensively to confirm its design goals, and is well suited as a torque-sensing device in robots or other industrial high performance motion control applications. A quantitative comparison with di erent types of sensors is shown in table 1. The table indicates that our sensor's performance characteristics compare very favorably. The applications of adaptive control in conjunction with joint-torque sensory feedback was used for dynamic motion control of manipulators. The control system had the advantages of requiring neither the computation of link dynamics nor the precise measurement of joint torques, i.e., the torque sensor's gains and o sets are unknown to the controller. The adaptive controller could also tune all the joint parameters including the rotor inertia, twist
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".