Intuitive BIM-aided robotic navigation and assets localization with semantic user interfaces
Bibliographic record
Abstract
Introduction: The deployment of mobile robots on construction sites has gained increasing attention from both academic research and industry due to labor shortages and the demand for more efficient project management. However, integrating robotic systems into dynamic and hazardous construction environments remains challenging. Key obstacles include reliance on extensive on-site infrastructure, limited adaptability, and a disconnect between system capabilities and field operators' needs. Methods: This study introduces a comprehensive, modular robotic platform designed for construction site navigation and asset localization. The system incorporates Building Information Modeling (BIM)-based semantic navigation, active Ultra-Wideband (UWB) beacon tracking for precise equipment detection, and a cascade navigation stack that integrates global BIM layouts with real-time local sensing. Additionally, a user-centric graphical user interface (GUI) was developed to enable intuitive control for non-expert operators, improving field usability. Results: The platform was validated through real-world deployments and simulations, demonstrating reliable navigation in complex layouts and high localization accuracy. A user study was conducted, confirming improved task efficiency and reduced cognitive load for operators. Discussion: The results indicate that the proposed system provides a scalable, infrastructure-light solution for construction site robotics. By bridging the gap between advanced robotic technologies and practical deployment, this work contributes to the development of more adaptable and user-friendly robotic solutions for construction environments.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".