Bibliographic record
Abstract
Optimal ground conditions in mining are crucial for operational efficiency and safety. Motor graders play a crucial role in achieving precise grading of passages; however, unlike many other mining vehicles, they have not yet been commercially automated. The redundant kinematics of motor graders, including articulation, front axle steering, and blade operations, pose significant challenges for automation. This research explores path following with a 2D kinematic model of motor graders and uses simulation to evaluate and compare the performance of these controllers in terms of step response and operational effectiveness. To address kinematic redundancy, two methods are proposed: Rear Offset Control, which defines the articulation angle using a lateral offset between the front and rear axles, and Single Track Control, which utilizes steering redundancy to adapt existing automation approaches to articulated vehicles. Simulation tests were conducted using two controllers: a Feedback Linearized PD controller (FBL+PD) and a Feedback Linearized Model Predictive Controller (FBL+MPC). Both were tuned for optimal performance on a step input path, with performance assessed based on Root Mean Square Error (RMSE) and control effort. This work introduces two methodologies—Rear Offset Control and Single Track Control—for autonomous motor grader operation. These were implemented with FBL+PD and FBL+MPC controllers on a 2D kinematic model. Future research will focus on dynamic modeling, implementing a Non-Linear Model-Predictive Controller, refining tuning methodologies for Feedback Linearized systems, and integrating these approaches with live vehicles.
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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".