Performance Indices for Collaborative Serial Robots With Optimally Adjusted Series Clutch Actuators
Bibliographic record
Abstract
Safety is the first priority when designing robots that are intended to physically interact with humans. New robotics standards state as a condition for collaboration that the robot should be designed so that it cannot exert forces larger than 150 N at its tool center point. An effective and reliable way of guaranteeing that this force cannot be exceeded is to place a torque limiter in series with each actuator, thus forming series clutch actuators (SCAs). Since the relationship between the joint limit torques and the achievable end-effector forces is configuration dependent, it is preferable to use adjustable torque limiters. This paper presents a method to optimally control the limit torques of a serial manipulator equipped with adjustable series clutch actuators. It also introduces two performance indices to evaluate the quality of the relationship between the joint limit torques and the achievable end-effector forces. The first one is the ratio of the minimum and maximum force thresholds. Even if it has a strong physical meaning, it is not differentiable everywhere in the workspace and is thus difficult to use in an optimization process based on its gradient. A second index, smooth, and expressed in a closed-form, is therefore introduced which is the determinant of the normalized Jacobian matrix postmultiplied by its transposed. Examples of redundant manipulator motion optimization and of collaborative robot architecture optimization using the second index are shown. The limitations of the proposed approach are that it is based on a static model—which is nevertheless valid under the current safety standards—and that gravity is neglected.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".