Guaranteed Collision-Free Reference Tracking in Constrained Multi Unmanned Vehicle Systems
Bibliographic record
Abstract
In this article, we face the reference tracking control problem for a system of heterogeneous multiple unmanned vehicles (MUVs) moving in a 2-D planar environment. We consider a scenario, where each vehicle follows a trajectory imposed by a local planner and where each unmanned vehicle can have different linear dynamics as well as different constraints and disturbances. In this contest, we design a novel control architecture, where a centralized traffic manager, in conjunction with ad-hoc designed local vehicle controllers, is capable of ensuring the absence of collisions. The proposed solution is obtained by exploiting, for the local vehicles’ controllers, a dual-mode model-predictive controller and, for the traffic manager, set-theoretic and controllability properties. Moreover, after modeling the potential vehicle collisions with a graph, connectivity arguments are used to obtain an optimal collision resolution, which minimizes the number of vehicles that need to be stopped. The resulting control scheme ensures collision-free signal tracking. Results of the simulation conducted on an MUV system are shown to provide tangible evidence of the features of the proposed framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".