Path-Following Controller Designs for Autonomous and Semi-Autonomous Industrial Motor Graders
Bibliographic record
Abstract
Haulage road maintenance is crucial for operational efficiency and safety in mining and construction activities. Industrial motor graders play a key role in this task, both on surface and in underground mines, where production vehicles—such as trucks and loaders—are increasingly being driven autonomously. However, motor graders have yet to be commercially automated. The redundant kinematics of motor graders, including articulation, front-axle steering, and blade operations, pose technical challenges for autonomy. In this work, we leverage the steering redundancy of motor grader designs to formulate a new path following controller that is compatible with existing approaches for the automation of articulated vehicles. The proposed methodology, coined “Single-Track Control”' (STC) allows for coordination of both the front-axle steering angle and the vehicle’s articulation angle to keep the front and rear wheels on a common track. This innovation mitigates the risk of collisions with drift walls and improves manoeuvrability. It can be used for semi-autonomous operations, to reduce the complexity for operators, as well as for fully autonomous operations. The approach was validated in simulation, comparing the implementation performance of two controller types.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".