Design and Assessment of an Industrial Maintenance Assistance System Based on Mixed Reality
Bibliographic record
Abstract
Maintenance, storage and warehousing are complex processes required in many industries such as automotive, aerospace, manufacturing and logistic companies.These processes, often, involve moving objects in crowded environments using robots or human operators.Particularly, replacement and assembly of machine parts in crowded environments when performed by a human being require high technical skills.These tasks may be performed using robots to reduce costs due to human errors and execution time.However, robots under open world assumptions could neither operate in all environments nor perform tasks not modeled by the designer.In this paper, we introduce a mixed reality system to assist human operators in moving objects in crowded environments for maintenance tasks such as: parts assembly or replacement, and storage of objects.The introduced system consists of a mobile application exploited through a hands-free VR box.The proposed Mixed Reality for Industrial Maintenance (MRIM) system enhances the perception of a human operator by overlaying 3D real world visual information and virtual objects, such as: orientation guidelines including rotating angles, moving direction and displacement of carried objects.These guidelines allow for gaining execution time, and reducing human errors that might cause industrial parts damage.The proposed work brings two main contributions.First, it makes use of a new algorithm based on recasting, named R star (R*) that allows for optimizing pathfinding in 3D space.This later outperforms the two commonly used baseline 3D pathfinding algorithms of at least 87.5% in terms of execution time.Second, MRIM provides an easy-to-use interface that exploits information provided by the R* algorithm.The experiments, conducted in real condition for the task of part replacement in a crowded environment, show that MRIM reduces considerably execution time and human errors.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".