Proposed Standards and Tools for Risk Analysis and Allocation of Robotic Systems to Enhance Crew Safety during Planetary Surface Exploration
Bibliographic record
Abstract
Several space agencies have announced plans to return humans to the Moon in the near future. The objectives of these missions include using the Moon as a stepping-stone towards crewed missions to Mars, to test advanced technology, and to further exploration of the Moon for scientific research and in-situ resource utilization. To meet these objectives, it will be necessary to establish and operate a lunar base. As a result, a wide variety of tasks that may pose a number of crew health and safety risks will need to be performed on the surface of the Moon. Therefore, to ensure sustainable human presence on the Moon and beyond, it is essential to anticipate potential risks, assess the impact of each risk, and devise mitigation strategies. To address this, a nine-week intensive investigation was performed by an international, interdisciplinary and intercultural team on how to maximize crew safety on the lunar surface through a symbiotic relationship between astronauts and robots. To identify if and how robotic systems may be employed to enhance safety, it was necessary to establish a standardized risk assessment criteria. Risk assessment criteria used by space agencies and health and safety institutions from around the world were combined and integrated to meet this requirement. In addition, a decision tree was developed to rapidly identify robotic platforms that will enhance crew safety during tasks that require astronaut participation. Using these tools it was possible to appropriately identify the impact of each risk on crew safety in terms of the probability of occurrence (likelihood) and consequence (severity), and generate a list of robotic platforms to improve crew safety. Furthermore, the risk assessment criteria and robotic decision tree are extensible to lunar and Mars exploration missions regardless of mission architecture.
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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 | 0.053 | 0.109 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".