Computer Vision for Safe Human-Robot Collaboration in Disassembly: A Systematic Review and Conceptual Framework
Bibliographic record
Abstract
Disassembly, the first stage in remanufacturing, benefits from human-robot collaboration, which improves efficiency and accuracy. ISO 10218-2:2011 mandates human safety, while ISO/TS 15066:2016 emphasizes risk assessments in cobotic systems. This review examines computer vision techniques—object detection, depth sensing, and trajectory planning—for real-time human and cobot arm detection, achieving over 96% accuracy in object recognition and response times under 50 milliseconds, and is robust to environmental factors such as dust. We propose a hybrid approach integrating these techniques to enhance hazard detection, real-time monitoring, and adaptability in HRC disassembly tasks. This framework addresses gaps in dynamic risk assessment, enabling precise hazard identification in unstructured settings and improving human-cobot interaction monitoring. Supported by key performance indicators such as detection accuracy, response time, and adaptability, this approach offers a scalable and robust solution for diverse disassembly scenarios, advancing both safety and efficiency in HRC systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".