World representations for unmanned vehicles
Bibliographic record
Abstract
Unmanned vehicles (UxV) operate in numerous environments, with air, ground and marine representing the majority of the implementations. All unmanned vehicles, when traversing unknown space, have similar requirements. They must sense their environment, create a world representation, and, then plan a path that safely avoids obstacles and hazards. Traditionally, each unmanned vehicle class used environment specific assumptions to create a unique world representation that was tailored to it operating environment. Thus, an unmanned aerial vehicle (UAV) used the simplest possible world representation, where all space above the ground plane was free of obstacles. Conversely, an unmanned ground vehicle (UGV) required a world representation that was suitable to its complex and unstructured environment. Such a clear cut differentiation between UAV and UGV environments is no longer valid as UAVs have migrated down to elevations where terrestrial structures are located. Thus, the operating environment for a low flying UAV contains similarities to the environments experienced by UGVs. As a result, the world representation techniques and algorithms developed for UGVs are now applicable to UAVs, since low flying UAVs must sense and represent its world in order to avoid obstacles. Defence R&D Canada (DRDC) conducts research and development in both the UGV and UAV fields. Researchers have developed a platform neutral world representation, based upon a uniform 21/2-D elevation grid, that is applicable to many UxV classes, including aerial and ground vehicles. This paper describes DRDC's generic world representation, known as the Global Terrain map, and provides an example of unmanned ground vehicle implementation, along with details of it applicability to aerial vehicles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".