Collision Detection for Human-Robot Interaction in an Industrial Setting using Force Myography and a Deep Learning Approach
Bibliographic record
Abstract
By applying robots while collaborating with a human in an industrial setting to provide more flexible and productive industries, safe interaction and collision detection have become an indispensable element of the collaborative robots. In such a dynamic environment, safe collaboration scenarios are needed to be designed using reliable methods to monitor collision-related signals and avoid a dangerous collision. Since human's hand is the most exposed limb to collision during cooperation with a robot, new flexible methods should be conducted to use in industries by considering hand safety. In this study, collision monitoring is developed using force myography of a worker forearm and robot dynamic parameters. A method based on deep neural network is proposed to distinguish any occurrence of a collision between a worker's hand and robot's arm during the collaboration. The proposed approach can be applied to provide a reliable interaction with no unnecessary robot stop during working by classifying unintended collision. Various experiments have been conducted to evaluate the proposed method. The results show that the proposed scheme can successfully detect a collision and classify human intention to provide safe and reliable cooperation with a robot in an industrial environment.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".