Magnetorheological-Actuators: An Enabling Technology for Fast, Safe, and Practical Collaborative Robots
Bibliographic record
Abstract
Collaborative robots are more and more used in applications requiring robots and humans to work in proximity or direct contact. However, conventional collaborative robots powered by servo-geared actuators are intrinsically dangerous due to their high reflected inertia. Recent studies have shown that low inertia and high bandwidth (> 30 Hz) magnetorheological (MR) actuators have the potential to improve the safety of collaborative robots without reducing their force and speed capabilities. The main contribution of this paper is to provide a quantitative assessment of how MR actuators can contribute to reducing the impact forces with humans, and thus increase the safety of collaborative robots. Dynamic models, validated with simplified 1 DOF experiments, show that the safety level of collaborative robots can be increased by a factor up to 3 only by changing the conventional servo-geared actuator architectures for MR actuators with no other changes. The paper also presents a simple, reliable, and fast collision detection method based on joint angular velocity band-pass filtering, a method exploiting the unique low inertia and clean dynamics properties of MR actuators. Finally, an experimental comparison of representative collaborative robots demonstrates an impact force reduction of 10 times using MR actuators, fast collision detection, and passive foam padding.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".