Real-time sustainable cobotic disassembly planning using fuzzy reinforcement learning
Bibliographic record
Abstract
Collaborative robots (cobots) play a vital role in smart manufacturing, particularly in disassembly processes. Human-robot collaboration (HRC) methods simultaneously leverage the complementary capabilities of humans and cobots, offering promising improvements in disassembly processes. A review of the literature reveals that most proposed HRC disassembly planning models do not incorporate sustainable factors, such as consumed energy, human safety, ergonomic risks, and circularity, in the decision-making process. Furthermore, uncertainties inherent in disassembly processes, such as the quality of recovered parts, are not well-addressed in the literature. This paper presents a novel multi-agent fuzzy reinforcement learning (RL) planning model for sustainable HRC disassembly. In addition to cost elements, the developed model involves social and environmental considerations in the real time planning process. By developing a fuzzy-based environment in the RL architecture, the proposed approach aims to effectively model the probabilistic uncertain parameters involved in the problem. Experimental analysis shows that the model presented in this research outperforms a baseline model, applied to the same case study, in terms of convergence time. Furthermore, in terms of qualitative analysis, the proposed model integrates a more extensive set of features into the planning process compared to recent literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".