2 DOF cartesian force limiting device for safe physical human-robot interaction
Bibliographic record
Abstract
This paper presents a device that significantly increases the safety level of suspended robots whose end-effector orientation remains constant with respect to the vertical direction (e.g. Scara-type suspended robots). The device is a two-degree-of-freedom (DOF) parallel mechanism with a parallepipedic architecture on which two revolute joints have been replaced with commercially available torque limiters. The device is implemented as a mechanical connection between the robot and the effector. It is rigid unless excessive horizontal forces are applied on the end-effector, for example during a collision. The level of force that activates the mechanism is set by properly adjusting the threshold of the torque limiters. Furthermore, a collision can be rapidly detected with a limit switch placed on one of the links of the mechanism and a signal can be sent directly to brakes that will stop the robot, without passing through a controller and thus improving the reliability and reaction-time of the safety system. By mechanically disconnecting the robot from its end-effector, the device ensures that the person involved in the collision is only subjected to the inertia of the end-effector and thus potential injuries are greatly reduced. A prototype of the proposed device has been built to validate the concept and to study its behaviour for collisions with different velocities and orientations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".