Impact Force Reduction Strategies To Achieve Safer Human-Robot Collisions
Bibliographic record
Abstract
The increasing use of robots operating close to people has made human-robot collisions more likely.In this paper, strategies intended to reduce the impact force to a safe level, without sacrificing the robot's performance, are investigated.The strategies can be applied to a robot arm without modifying its internal hardware.They include the existing strategies: lowering the actuator controller's stiffness; actuator switched off upon impact detection; withdrawing the arm upon impact detection; and adding a compliant cover.We also propose the novel strategy of limiting the controller's feedback term.The collision scenario studied is a robot arm colliding with a person's constrained head.An improved lumped parameter model of the constrained impact is proposed.Simulation results are included for a UR5 collaborative robot.Sixteen combinations of the impact force reduction strategies are compared.The results show that using a high stiffness controller with a feedback limit and compliant cover reduces the impact force to a safe level, and achieves precise 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".