Lunar construction: A state-of-the-art survey
Bibliographic record
Abstract
The renewed global interest in furthering human’s presence on the Moon has catalyzed efforts to establish a sustainable lunar base. The incentive is not only for scientific opportunities and prospects of deep-space exploration, but also for demonstrating technologies that will extend our reach throughout the Solar System. Central to such efforts is the development of robust and scalable lunar construction technologies. This survey presents a comprehensive review of the state-of-the-art in lunar construction, including environmental characterization, infrastructure development, construction methods and materials, and robotic systems. The unique challenges posed by the lunar environment are highlighted, such as extreme temperature variations, high radiation exposure, and micrometeorite impacts, with a particular emphasis on the abrasive, adhesive, and electrostatically charged lunar regolith, thus including strategies developed for lunar dust mitigation. The survey investigates the critical infrastructure that will need to be established, including habitats, power stations, communication stations, landing pads, blast berms, and more. A detailed analysis of the methods and materials that are being developed to create such infrastructure is presented, identifying which methods have demonstrated promise and garnered the most attention. A diversity of robotic technologies are required to enable the construction of the necessary infrastructure using these methods and systems, which are broken down into lunar cranes, mobile manipulators, 3D printers, and robot teams, with a particular focus on work being done to develop flight systems. The paper concludes by identifying critical research and technological gaps that must be addressed to support the next generation of lunar missions and long-term extraterrestrial habitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".