Characterizing the Effect of Reduced Gravity on Rover Wheel-Soil \nInteractions
Bibliographic record
Abstract
The entrapment of the Mars Exploration Rover Spirit in soft regolith and the tears and punctures in the Mars Science Laboratory Curiosity rover’s wheels demonstrate some of the current mobility challenges in granular terrains on extraterrestrial planetary surfaces. \n \nClassical wheel-terrain interaction models used in the literature are unable to sufficiently predict the effects of reduced-gravity on rover performance. Several researchers today highlight the insufficient predictive power of classical terramechanics models for planetary rovers, thus implying a need to renew the experimental underpinnings of our theories. Only a single dataset has been reported in the literature for wheels driving in soil during reduced-g flights, and the actual data collected is limited. This thesis presents data that more than doubles the number of existing reduced-g wheel-soil interaction experiments for the study of terramechanics. One of the key contributions is that it includes the measurement of drawbar pull (i.e. net traction force) data as well as direct observation of wheel-soil interactions (through a glass sidewall), both for the first time ever in reduced gravity. \n \nThe experimentation campaign is designed to inform the upcoming ExoMars space mission, through the use of ExoMars wheel prototype and Martian soil simulant in simulated Martian gravity produced in parabolic flights. An advanced automated gantry system is developed to support this activity with improved control and repeatability over the prior published experiments. In addition to Martian gravity, wheel-soil interactions are also studied in Lunar gravity, all achieved aboard Canada’s National Research Council’s (NRC) Falcon 20 aircraft. Wheel rotation rate, horizontal advance rate, and vertical wheel loading are controlled independently. To address the constraints imposed by testing aboard an aircraft performing parabolic flights and to achieve experimental repeatability and consistency, a novel rapid automated soil preparation subsystem is developed. The consistency and repeatability of the soil preparation are studied and verified both through cone penetration tests and through examining triplicates of terramechanics (i.e. traction force, wheel sinkage) datasets. \n \nA key observation from the terramechanics dataset is a significant reduction of traction (over 30\\% less) in partial gravity experiments (PGE) compared to on-ground experiments (OGE), at the same wheel loading. The complementary visualization analysis results indicate that, with wheel normal load held equal between experiments, the amount of soil mobilized by wheel-soil interaction substantially increases as gravity decreases. The results of the visualization analysis suggest a deterioration in the soil strength at lower gravities, which thus undermines the rover mobility by reducing the net traction. The results have important implications regarding the practice of using a reduced-mass rover on Earth to assess the performance of a full-mass rover in similar soil on a reduced-gravity surface. Other details discovered in the dataset are also further elaborated in this study. \n \nThe analysis of terramechanics data and high-speed images that are collected at Lunar and Martian gravities, and contrasted against OGE, not only guide the understanding of the influence of gravity on wheel performance but also holds promise to fill the gaps of research in the literature. The congruity of analysis of computer vision/clustering techniques with terramechanics results in this campaign highlights a promising technique for studying these interactions in a planetary context. The richness of the data produced, unprecedented in the study of robot-terrain interactions, can highlight gaps and discrepancies in existing models and enables validation of new models that approach robot-terrain interactions with an appropriate and efficient level of detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".