Lunar Robotic Construction System Using Raw Regolith: Design Conceptualization
Why this work is in the frame
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Bibliographic record
Abstract
This paper outlines the inception, conceptualization and primary morphological selection of a robotic system that employs raw lunar regolith for constructing protective berms and shelters on the Moon’s surface. The lunar regolith is considered the most readily available material for in situ resource utilization on the Moon. The lunar environment is characterized, and the operational task is defined, informing the development of high-level system requirements and a functional analysis through the glass-box method. The key morphological areas are identified, and candidate concepts are evaluated using the Analytic Hierarchy Process (AHP). The evaluation process employs a new approach to aggregating expert data through the ZMII method to establish priorities of the design criteria, which eliminates the need for pairwise comparisons in data collection. Each criterion is associated with a specific and quantifiable metric, which is then used to evaluate the morphologies during the AHP. The selected morphologies are determined as: a vibrating hopper for intake (normalized decision value of 27.5% out of 5 candidate solutions), a roller system for container deployment and filling (26.2% out of 7), a magnetic RCU interface (22.6% out of 7), and a 4-DoF manipulator to place the RCUs in the environment (23.6% out of 5). The final morphology is selected by combining the decision values across the primary morphological areas into a unified decision metric. This is followed by the preliminary selection of the system’s surrounding architecture. The design conceptualization is performed within a real-life operational scenario, namely, to create a blast berm for the landing pad using the lunar regolith provided by an existing excavator. The next phase of the work will include the system’s detailed design, as well as investigations on the requirements for a variety of construction tasks on the lunar surface.
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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 | 0.000 | 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 | 0.000 | 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 it