IAC-07-A5.2.03 ROBOTIC ASSISTANCE, MOBILITY & VISION SYSTEMS – ENABLING TECHNOLOGY FOR EARLY HUMAN-ROBOTIC LUNAR EXPLORATION
Bibliographic record
Abstract
In the 21 st Century, space exploration will focus on surface exploration, with human-robotic collaboration allowing extended-duration stays at challenging lunar and planetary sites. This paper focuses on three early phases of lunar exploration: (i) in-situ characterisation and site survey for science, prospecting and reconnaissance in advance of human sorties (ii) in-situ human sortie field assistance, and (iii) transport of samples, equipment and small-scale infrastructure. In particular two technologies relevant to these phases are examined: surface mobility and advanced vision. For surface mobility, three areas of MDA technology development are discussed: (i) rover chassis simulation and evaluation, (ii) chassis prototype construction and field testing, and (iii) autonomous rover navigation systems for localisation, motion estimation and path planning. An overview of recent activities is given, including the development of a prototype chassis in preparation for the ESA ExoMars mission, and field tests of camera- and lidar-based navigation technologies in partnership with Optech, NASA and the Canadian Space Agency (CSA). The application of vision systems for navigation, science, prospecting and site survey is discussed. The merits of camera- and lidar-based systems are summarized, along with their applicability to the Moon in rover-mounted and astronaut handheld scenarios. 1
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.145 |
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".