2023 EELS Field Tests at Athabasca Glacier as an Icy Moon Analogue Environment
Bibliographic record
Abstract
JPL is developing a versatile and highly intelligent Exobiology Extant Life Surveyor (EELS) robot that would enable access to subsurface oceans and near-surface liquid reservoirs through existing conduits, such as the vents at the south pole of Enceladus or the putative geysers on Europa. A key mobility requirement for future vent exploration missions will be the ability to carefully descend and hold position in the vent to collect and analyze samples while withstanding plume forces without human intervention. Furthermore, this must be accomplished in a highly uncertain environment, requiring versatile hardware and intelligent autonomy. To work towards that goal, we have prototyped the EELS 1.0 and EELS 1.5 robots for horizontal and vertical mobility, respectively, in icy terrain. Autonomous surface mobility of EELS 1.0 was previously validated in a variety of terrain, including snowy mountains, ice rinks, and desert sand. Vertical mobility of EELS 1.5 was developed on laboratory ice walls. This paper presents the first mobility trials for both robots on large-scale, natural icy terrain: the Athabasca Glacier located in Alberta, Canada, a terrestrial analogue to the surfaces and subsurfaces of icy moons. This paper provides a preliminary written record of the test campaign’s four major trials: 1) surface mobility with EELS 1.0, 2) vertical mobility with EELS 1.5, 3) science instrument validation, and 4) terramechanics experiments. During this campaign, EELS 1.5 successfully held position and descended ~1.5 m vertically in an icy conduit and EELS 1.0 demonstrated surface mobility on icy surfaces with undulations and slopes. A miniaturized capillary electrophoresis (CE) instrument built to the form factor of an EELS module was tested in flowing water on the glacier and successfully demonstrated automated sampling and in-situ analysis. Terramechanics experiments designed to better understand the interaction between different ice properties and the screws that propel the robot forwards were performed on horizontal and vertical surfaces. In this paper we report the outcomes of the four tests and discuss their implications for potential future icy missions. The field test also demonstrated EELS’s ability to support Earth science missions. Another potential near-term follow-on could be a technology demonstration on the Moon. This paper is a high level report on the execution of the field test. Data and results will be detailed in subsequent publications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".