<title>General framework for group robotics with applications in mining</title>
Bibliographic record
Abstract
Worker safety is ofparamount importance in industries where harsh working environments are the norm. The research being conducted by this group aims to take workers out ofharm's way by creating an automated control system for fleets of intelligent machines that will do the dirty work, while the humans oversee the system to ensure proper operation. The focus of the research is not on machine intelligence, but rather on the system that will control the fleet in unstructured or semistructured locales. Mine environments are used as a target for this research, which is broken into a few main sections. The first section deals with dynamically creating task schedules for the vehicles based on possibly changing environmental conditions. The second section deals with resource sharing between multiple vehicles, especially the sharing ofroadways to ensure operational safety. This is done using Petri net data structures and theory. Thirdly, since the machines may not be able to independently cope with obstacles they encounter, human intervention capability is required. Fourthly, for human operators to make sense of the system's overall state and requests, development of a human-machine interface is necessary. Experiments have been conducted which demonstrate the successful use ofthis framework to control two model-size intelligent machines given one shared resource, namely a two-road intersection. In the future the group intends on imposing greater loads on the system (i.e. more vehicles and shared resources), and on integrating more complex human intervention capabilities, finer vehicle control, and improved system state monitoring.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.024 |
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".