Autonomous system for navigation and surveying in underground mines
Bibliographic record
Abstract
Abstract This paper describes an autonomous platform for navigation and surveying within networks of tunnels, as those typically found in underground mines and caves. In this context, we propose a system allowing two modes of operation: surveying and autonomous navigation mode. In the surveying mode, a remotely located supervisor instructs the platform to move through successive sections of the network, gathering range data that is then concatenated into two‐ and three‐dimensional survey maps of the environment. In navigation mode, the supervisor specifies high‐level missions using the previously acquired survey maps. A motion planner then translates each mission into a set of consecutive navigation actions separated by natural landmarks. Mission execution consists of autonomously detecting landmarks, self‐localizing, and performing the planned navigation actions. Advanced and innovative features, mostly related to surveying capabilities, navigation mode switcher, and the integrated aspect of our system, distinguish it from navigation systems used in productive mining vehicles described in the literature. The proposed system starts sharing some functionality with robotics systems devoted to exploration of abandoned mines. The full functionality of the navigation/surveying system has been proven through a series of on‐site experiments in underground mines. © 2007 Wiley Periodicals, Inc.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".