Evaluating the Use of Unoccupied Aircraft Systems (UASs) for Planetary Exploration in Mars Analog Terrain
Bibliographic record
Abstract
Abstract Planetary analog mission simulations are essential for testing science operations strategies and technologies. They also teach us how to use terrestrial analogs to inform studies of extraterrestrial environments. Unoccupied aircraft systems (UASs) have great potential for planetary surface exploration as demonstrated by the Mars 2020 Ingenuity helicopter and the in-development Dragonfly mission to Saturn’s moon Titan. Although applications of UAS technology for planetary exploration remain largely unexplored, simulated missions in planetary analog terrains can inform operational best practices. As part of the Rover–Aerial Vehicle Exploration Network project, we simulated a 12 sol UAS mission on Mars in the Holuhraun region of Iceland. The UAS had airborne imaging capability, as well as imaging, sampling, and geochemical analysis capabilities while landed. The mission evaluated the use of these instruments and developed operational strategies for using UASs to explore a planetary surface. Oblique airborne images were essential for mission planning and were used to scout large areas to identify both potential landing sites and targets for focused investigations. The airborne and landed data collected by the UAS allowed for detailed observations and interpretations not possible with analog orbital data sets, resulting in an improved scientific return for the simulated UAS mission compared to a premission analysis of only the analog orbital data. As a planetary exploration vehicle, a UAS is most advantageous for exploring large areas (many square kilometers) and is particularly useful when the terrain may be impassable to ground-based traverses (e.g., by rovers or humans).
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".