Dynamic role-adaptive collaborative robots for sustainable smart manufacturing: an AI-driven approach
Bibliographic record
Abstract
Purpose The study aims to address critical challenges in collaborative robotics, focusing on dynamic role adaptation, efficient task planning and sustainability. The primary goal is to develop a framework that enhances cobots’ ability to adapt to changing tasks, collaborate effectively with human operators and contribute to sustainable manufacturing practices. Design/methodology/approach An innovative framework leveraging artificial intelligence (AI) and advanced machine learning techniques was developed to enable dynamic role adaptation in cobots. The framework was validated through experimental evaluations conducted in a simulated industrial environment using Gazebo. Performance metrics, including task efficiency, energy consumption and material waste, were analyzed to assess the framework’s effectiveness. Findings The experimental results demonstrated that the proposed framework improved task efficiency by 25%, reduced energy consumption by 20% and achieved significant reductions in material waste. These outcomes highlight the framework’s potential to optimize manufacturing operations while promoting sustainability. Originality/value This research introduces a novel AI-driven approach to collaborative robotics, integrating dynamic adaptability and sustainability metrics into cobot operations. By addressing the dual objectives of productivity and environmental impact, the framework advances the state-of-the-art in intelligent manufacturing systems and offers practical solutions to pressing industrial challenges.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".