Coordinated path-following control for a group of mobile robots with velocity recovery
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the problem of coordinated path following where multiple mobile robots are required to follow a prescribed path while keeping a desired inter-robot formation pattern. A combination of the Lyapunov techniques and graph theory is used to derive the formation architecture. Path following for each vehicle consists of converging the geometric error at the origin. Vehicles' coordination is achieved by adjusting the speed of each vehicle along its path according to information on the positions and speeds of a subset of the other vehicles of the group. Unlike previous research that assume availability of the reference velocity to each mobile robot, the situation is considered where this information is only available to a leader of this formation. The control scheme relies on an adaptive design to estimate the reference velocity which the other mobile robots need to reconstruct to recover the desired formation. Simulations results are presented and discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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