Deploying pickers and robots in cobot-based collaborative order picking systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a promising application of cobots in labor-intensive warehouses, human-robot collaborative order picking systems provide a flexible and human-friendly picking solution by capitalizing on the best attributes of human pickers and robots. Few studies have determined operation modes of human-robot collaborative order picking systems to be beneficial to efficiency, cost, and the well-being of human workers. We identify four human-robot collaborative modes for order picking: single robot to single picker (Couple), single robot to multiple pickers (SR-to-MP), single picker to multiple robots (SP-to-MR), and multiple pickers to multiple robots (MP-to-MR). For each mode, we establish a fork-join queueing network model to analyze system performance and apply a fatigue-recovery model to estimate the fatigue of the pickers. The proposed fork-join queueing network model and fatigue-recovery model are validated by simulation. Although the throughput time and picker fatigue in the SR-to-MP mode can benefit from an appropriate zoning policy, we find, interestingly, that the zoning policy cannot reduce the throughput time in the SP-to-MR mode. The SP-to-MR mode is economical if a warehouse does not pursue a swift throughput time. A well-capitalized warehouse can adopt the SR-to-MP mode to improve the throughput time further in a more human-friendly manner.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 it