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 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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".