High-fidelity Software-in-the-loop Simulation of a Six-wheel Lunar Rover using Vortex Studio for Output-tracking Control Design
Bibliographic record
Abstract
Six-wheel autonomous rovers with skid-steering rear wheels have been designed for Lunar exploration programs due to their lightweight and their enhanced traction and stability. In this paper, a Software-in-the-loop Simulation (SILS) is presented for such a system containing a controller coded in MAT-LAB and a digital twin of the system modeled in Vortex Studio. The controller is developed based on the system's governing equations and static state feedback linearization to perform an output-tracking control task. The equations of motion are derived using Lagrange-d’ Alembert principle subject to ideal nonholonomic constraints and under the point-contact assumption at all wheels. Such simplifying assumptions are commonly considered in proposed control strategies for autonomous rover systems in the literature. The digital twin of the rover is modeled as a multi-body system with realistic parameters moving on 3-dimensional soft/rough terrains with arbitrary tire models provided by Vortex Studio. The results of the developed SILS are compared to those of a 2-dimensional simulation that is fully coded in MATLAB under the simplifying assumptions (ideal plant). This comparison discloses often existing discrepancies between real rover systems and their commonly used mathematical models. This study reveals the effects of isolated physical phenomena, e.g., wheel-slip and tractive force distribution, on the control performance, and can be utilized to design enhanced controllers for rover systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".