Control of an unconventional VTOL UAV for search and rescue operations within confined spaces based on the MARC control architecture
Bibliographic record
Abstract
This paper presents a Modular Architecture for Robotic Control (MARC) for unconventional Unmanned Vehicle Systems (UVS) with search and rescue applications. The MARC architecture is able to produce complex UVS behaviors with the interaction of multiple independent modular controllers, which are extremely difficult or impossible to execute via traditional control methodologies. This paper presents the implementation of the proposed architecture on a Double-Ducted Vertical Take-off and Landing (VTOL) vehicle, capable of performing complex maneuvers. The vehicle is designed specifically for flight within confined spaces. This paper also presents the results of the implementation showing that unconventional UVS can be used as optimal platforms for indoor flight executing diverse tasks such as obstacle avoidance and exploration. The results show that the proposed approach enables the test vehicle to fully use its flight characteristics which incorporate complex control in six Degrees of Freedom (DOF) for rapid navigation and "aggressive" maneuvers allowing large vehicular angles of attack. 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".