Multi-Agent-Based Approach for Real-Time Collision Avoidance and Path Re-Planning on Construction Sites
Why this work is in the frame
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Bibliographic record
Abstract
Collisions on construction sites are one of the major causes of fatal accidents. The complexity of equipment operations require detailed planning and better real-time control of the work. Research involving artificial intelligence in construction industry has been done to enhance communication between team workers and resolve distributed problems, for example, agent systems have been used for construction claims and dynamic rescheduling negotiation. However, little research has focused on real-time control of construction equipment operations using agents to improve safety on site. The present paper proposes a multi-agent-based approach to provide real-time support to the construction staffs. Collision avoidance is achieved by informing workers and equipment operators about potential collisions, and by providing replanning for equipment. A prototype system has been developed to and the functionalities of different agents are successfully tested.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it