Comparing Rover and Helicopter Planetary Mission Architectures in a Mars Analog Setting in Iceland
Bibliographic record
Abstract
Abstract The Rover–Aerial Vehicle Exploration Network project field-tested planetary mission operations within a Mars analog environment in Iceland using stand-alone rover and helicopter architectures. Mission planning, implementation, and results are reported for the rover mission and briefly summarized for the helicopter mission. The outcomes of both missions are subsequently compared. Field implementation occurred from 2022 July to August at the Holuhraun lava flow. The rover science operations team executed a 14 sol (Martian day) mission that achieved mission, science, and sampling goals, including the contextualization, acquisition, and planned caching of two eolian and two rock samples. The helicopter science operations team executed a plan of comparable length but emphasized different science goals given long-range flight capabilities and landing limitations. The resolution and targetability of the rover payload enabled more detailed analyses, whereas the helicopter was better able to map flow-scale morphologies. The rover’s exploration was limited by daily mobility duration limits and hazardous terrain, whereas the helicopter’s exploration was constrained by landing site hazards. Resource limitations resulted from lengthier rover drives and data-volume-intensive helicopter imaging surveys. Future missions using combined rover–helicopter architectures should account for each spacecraft’s resource needs and acknowledge system strengths in different geologic settings. Both missions served to establish operations strategies and mission outcomes to be applied to future combined rover and helicopter mission architectures, while the helicopter mission also evaluated strategies and outcomes for future stand-alone airborne missions. Findings in this work are relevant to future missions seeking to optimize strategies for planetary mission operations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".