Dynamic Sampling and Control for Automated Road Pre-Marking Robot
Bibliographic record
Abstract
This study introduces a specialized pre-marking robotic system that boasts a high degree of autonomy in response to low efficiency and inaccuracy in pre-marking operations for road delineations on newly constructed roads. The system is designed for autonomous navigation and precise spray-painting of road markings. It employs dynamic point sampling technology, enabling continuous and real-time acquisition of road coordinate information, thereby significantly improving pre-marking efficiency. A three-point circle correction method is implemented to generate the robot’s target path that includes curvature information. A curvature-adaptive pure pursuit control strategy is executed to ensure high-precision tracking of the pre-marking robot along the target path. Simulation experiments have confirmed the effectiveness and reliability of the robotic system. Practical applications reveal a marking error of less than 1.5 cm in long curved road scenario and 2 cm in right-angle curve road scenario. This result achieves efficient and accurate pre-marking operations and provides substantial technical support for road construction and maintenance.
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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 | 0.000 |
| 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 | 0.000 |
| 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".