Traction Control of Planetary Rovers on Prescribed Trajectories with Wheel-Fighting Consideration
Bibliographic record
Abstract
To reliably localize and control planetary rovers, their controllers must keep the wheels away from traction loss. In this paper, a fast traction control system for rovers is developed that tracks dynamic trajectories, leveraging their redundant control directions. Trajectory-tracking performance is guaranteed by stabilizing an input–output linearized nonholonomic model of the system. A novel methodology is proposed to determine the control actions that optimally distribute the tractive forces among the wheels without affecting the tracking performance. The methodology uses the knowledge of wheels’ friction coefficients and estimation of normal and tractive forces based on a nonholonomic rover model. The novelty is in redefining the optimization problem in both lateral and longitudinal directions, which requires minimum information about wheel–ground interactions and leads to linear optimality conditions. The notion of required force/moment at the rover’s center of mass is proposed to define reference directions for tractive forces and isolate fighting wheels whose tractive forces are suppressed by finding suboptimal control actions. The proposed traction control system is implemented on a six-wheel rover modeled after the Lunar Exploration Light Rover, and its efficacy against the conventional pseudo-inverse solution is demonstrated in a software-in-the-loop simulation environment of hard frictional ground using Vortex Studio.
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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) | 0.001 | 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".