Experiments in feedback linearized iterative learning‐based path following for center‐articulated industrial vehicles
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper describes the design, industrial application, and field testing of a technique for autonomous wheeled‐vehicle path following that uses iterative learning control (ILC) in a feedback linearized space. One advantage of this approach is that ILC is used without having to employ approximate linearization at every time step. The main contribution of this paper is the unique field experiments that used two large industrial‐scale center‐articulated underground mining vehicles. The described field work not only tested the underlying technique on commercial vehicles, but also presents a method for parallel speed learning, wherein the speed is adjusted over subsequent learning trials to improve cycle productivity. Finally, presented are field results for an approach to prelearning through simulation before deployment in the field to reduce the initial path‐following errors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it