Industrial-Scale Autonomous Vehicle Path Following by Feedback Linearized Iterative Learning Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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