Tendon-Driven Manipulator Actuated by Magnetorheological Clutches Exhibiting Both High-Power and Soft Motion Capabilities
Bibliographic record
Abstract
Tendon-driven manipulators (TDMs) are capable of large workspaces and low link inertias in compact embodiments. However, as most TDMs are powered by electromechanical actuators, their performance is fundamentally limited by either the high output inertia of electric geared motors, or by the large volume and weight of direct-drive electric motors. To improve upon the conventional TDM designs, this paper presents a TDM actuated by magnetorheological (MR) clutches. The MR-TDM concept combines the advantages of lightweight electric geared motors with the high dynamic performance of MR clutches. The main objective of this paper is to investigate the overall performance of the MR-TDM and demonstrate its wide range of applications. Analytical studies show the higher open-loop natural frequency (60 Hz) and lower reflected output inertia of the MR-TDM compared to the TDM powered by traditional actuators. Furthermore, experiments with a 2-DOF MR-TDM prototype demonstrate the usefulness of the approach for a wide variety of tasks ranging from a soft and precise control to a high impulse response and fast trajectory tracking.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".