Optimization Architecture of a Modular Architecture for Robotic Control: MARC Control Structure Applied to a VTOL UAV
Bibliographic record
Abstract
This paper presents a modular architecture for robotic control (MARC) for unconventional unmanned aerial vehicles (UAV). MARC was developed for a double-ducted vertical take-off and landing (VTOL) vehicle, capable of maneuvering within densely obstructed environments. The focus of this paper is to present the characteristics of the MARC architecture for the use of a twin-duct VTOL as an optimal platform for indoor flight used for surveillance and reconnaissance missions. This paper describes how MARC utilizes dynamic weighting, from potential functions, to impact the influence of multiple independent task and action based control systems. MARC optimizes the influence of multiple control modules to enable complex behaviors based on user defined operational goals. The test vehicle possesses flight characteristics that incorporate optimal control in 6 DOF for rapid "aggressive" maneuvers for navigation. The results showcase the potential for the MARC control system to optimize the performance of mobile robotics, specifically unconventional vehicles.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".