Control Strategies for a Modular Assembly of Tetrahedral-Shaped Multirotor Drones
Bibliographic record
Abstract
This paper is interested in the control of tetrahedral aerial vehicles made of the assemblage of smaller tetrahedral vehicles. While a centralized control strategy can be employed to manage all actuators of the assembled system, this strategy may require software and hardware modifications every time modules are assembled. Here, we consider control strategies with a level of decentralization such that they do not require hardware and software modifications when assembling multiple modules together irrespective of how they are connected. To this end, we present two strategies: the first does not require the knowledge of the configuration of the assemblage, while the second, which offers better performance, includes identifying the configuration automatically during the takeoff phase of the flight. We discuss the control allocation associated with both strategies, as well as the configuration identification algorithm employed in the second strategy. Experimental analysis, including conducting flight tests, validates the efficacy and performance of the presented control strategies.
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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 | 0.001 | 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".