Α Behavior Trees-based architecture towards operation planning in hybrid manufacturing
Bibliographic record
Abstract
In modern manufacturing, the capability of process scheduling and task allocation is a major feature for the proper organization of complex production schedules. More particularly, the case of human-robot collaboration within assembly lines is considered as a quite challenging field, where an efficient process scheduling can reduce products’ delivery times, increasing in parallel its quality. The purpose of this paper is to propose an approach focusing on operation planning for Human-Robot Collaborative processes that consist of many tasks and multiple resources, such as the assembly of large-scale parts. The implementation of the Human-Robot Operation Planning (HROP) module is presented, which aim at the allocation of multiple operations between multiple and different types of resources. This development’s main pillar is a dynamic decision-making logic that combines both constraints, that exclude resources from the evaluation, as well as mathematical criteria, that provide finally a specific solution. The HROP particularity is that it is developed under the Behavior Trees (BT) architecture. For the validation of the proposed approach, a case study under a real industrial environment of the automotive industry is presented, based on the assembly of large-scale parts, such as buses, in a hybrid cell of both human operators and multi-type robots.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".