Industrial-Scale Autonomous Vehicle Path Following by Feedback Linearized Iterative Learning Control
Bibliographic record
Abstract
This work describes and demonstrates, through simulation and field trials, a technique for autonomous wheeled vehicle path following that uses iterative learning control (ILC) performed in a feedback linearized space to augment a base feedback linearization (FBL) path-following controller. The goal of ILC is to iteratively adjust steering rate inputs to account for unmodelled vehicle dynamics, environmental disturbances, and extreme path geometries. One fundamental advantage of this approach is that ILC can be used without having to employ approximate linearization at every time step, rendering the approach easily implementable and computationally inexpensive when compared with traditional approaches. The technique was validated by performing field trials using large industrial-scale autonomous underground mining vehicles. The presented work not only demonstrates the underlying technique in the field on commercial vehicles, but also proposes and validates a method for parallel speed learning, wherein the speed can be adjusted over subsequent learning trials to improve productivity. Finally, a method for pre-learning through simulation prior to deployment in the field is introduced in order to reduce initial path-following errors.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".