CHASSIS DESIGN & PERFORMANCE ANALYSIS FOR THE EUROPEAN EXOMARS ROVER
Bibliographic record
Abstract
The European Space Agency’s (ESA) ExoMars rover has recently been subject to a Phase A study led by EADS Astrium, UK. This rover mission represents a highly ambitious venture in that the rover is of considerable size ~200+kg with high mobility carrying a highly complex scientific instrument suite (Pasteur) of up to 40 kg in mass devoted to exobiological investigation of the Martian surface and sub-surface. The chassis design has been a particular challenge given the inhospitable terrain on Mars and the need to traverse such terrain robustly in order to deliver the scientific instruments to science targets of exobiological interest, We present some of the results and design issues encountered during the Phase A study related to the chassis. In particular, we have focussed on the overall tractive performance of a number of candidate chassis designs and selected the RCL (Science & Technology Rover Company Ltd in Russian) concept C design as the baseline option in terms of high performance with minimal mechanical complexity overhead. This design is a six-wheeled double-rocker bogie design to provide springless suspension and maintain approximately equal weight distribution across each wheel.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".