Guidage automatique d'un engin agricole par GPS cinématique
Bibliographic record
Abstract
La conduite des vehicules agricoles lors de travaux tels le semis, la pulverisation ou l'epandage, demande une attention particuliere de la part de l'agriculteur. La precision du guidage demandee engendre l'apparition d'une fatigue generale et visuelle, au detriment de la surveillance de la machine, des outils associes, ou bien encore de la qualite du travail agronomique realise. Dans cet article, nous presentons la possibilite de realiser cette tâche de conduite le long de trajectoires, qui ne sont pas necessairement des lignes droites, avec un systeme de guidage automatique utilisant un capteur GPS cinematique. L'orientation du vehicule est calculee a partir d'un estimateur d'etat, et une loi de commande non lineaire et independante de la vitesse est etudiee, reposant sur les proprietes des systemes chaines. Des experimentations sur le terrain, montrant les capacites de notre systeme de guidage, sont reportees et discutees.Driving agricultural vehicles during works such as sowing, pulverization or spreading, requests a particular care from the farmer. The required guidance precision creates the coming of a general and eye tiredness, to the detriment of the monitoring of the machine, the associated tools, or the quality of the agronomic work carried out. In this paper, we present the possibility of achieving this driving task along paths, which are not necessarily straight lines, with an automatic guidance system using a kinematic GPS sensor. The vehicle heading is derived according to a state estimator, and a non linear velocity independent control law is designed, relying on chained systems properties. Field experiments, demonstrating the capabilities of our guidance system, are reported and discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".