Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land
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Bibliographic record
Abstract
Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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