Towards Sustainable Human-Robot Collaborative Assembly: A framework Integrating Time, Safety, and Energy Efficiency
Bibliographic record
Abstract
The integration of collaborative robots (cobots) into assembly systems has transformed manufacturing by enabling flexible, adaptive, and human-centered production environments.However, designing and managing human-robot collaborative assembly lines remain a complex task, as multiple objectives must be balanced simultaneously.This paper addresses three interrelated dimensions: time efficiency, safety, and energy consumption.Time remains a critical drive of productivity, where synchronization of human and robot tasks directly influences cycle time and throughput.Safety is essential due to the shared workspace in which task allocation, interaction mode, and robot end effector and speed can either mitigate or intensify physical and mental risks to workers.Although cobots are typically more energy-efficient than traditional industrial robots, their cumulative energy use becomes significant at scale, particularly when deployed across multiple stations or operating continuously.Moreover, energy consumption is not independent: shorter cycle times often require higher accelerations and power demands, while slower, safety-oriented operations may reduce energy use but extend production duration.These interdependencies highlight the importance of considering energy not as a marginal factor, but as part of a broader trade-off with time and safety.Building on this perspective, a mathematical model is developed to integrate these objectives into a unified framework for humanrobot collaborative assembly.The contribution lies in advancing beyond single-objective optimization to capture the inherent trade-offs, offering insights that are aligned with emerging sustainability and safety standards.This approach provides a basis for future empirical validation and supports the development of resilient and sustainable human-robot collaborative systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".