Bibliographic record
Abstract
Planetary exploration rovers are the most efficient means of off-world surface exploration.As mobile laboratories, they are used to perform various experiments and gather data semi-autonomously from remote extraterrestrial environments, both for planetary science and assessing conditions in preparation for human exploration.To accomplish the mission and access sites of scientific interest, the rover must be able to traverse various types of unstructured terrain without becoming embedded or succumbing to other hazards.Modelling of the rover is essential to understand how the rover interacts with its environment and how to select the best path.This thesis presents the development of three-dimensional kinematic and dynamic models, using MATLAB and SimMechanics, describing the Argo J5 four-wheel rover, in response to terrain elevation inputs and slip.The kinematic models describe the pose and velocity of the rover using the Denavit-Hartenberg convention, while the SimMechanics dynamic model is combined with a terramechanics model to develop accelerations and obtain the forces and torques, based on terrain properties.The kinematic analyses were performed for simulated traverses including cases of flat, inclined, side slope, and sinusoidal terrain, with varying amounts of slip in the velocity analysis.The results showed good agreement with expected trends and values for the joint displacements and rates, with the largest percent deviation for the distance travelled being approximately 0.4 %.The results of the combined dynamic and terramechanics model, incorporating slip, are limited to the conceptual development of the model due to time constraints, and are thus inconclusive at this time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".