Trans-Domain Amphibious Unmanned Platform Based on Coaxial Counter-Propellers: Design and Experimental Validation
Bibliographic record
Abstract
The rapid evolution of the Unmanned Aerial Vehicle (UAV) industry has significantly increased interest in UAV design with trans-domain capabilities. It is still a major challenge to achieve miniaturization and enhance the maneuverability and underwater reliability of trans-domain UAVs. In this paper, a novel bullet shape Trans-Domain Amphibious Vehicle (TDAV) is proposed which achieves free trans-domain motion and has the advantages of small size, high maneuverability and high reliability for both rotary-wing UAV and Autonomous Underwater Vehicle (AUV) operation. Compared with traditional amphibious machines, the TDAV design is streamlined and thus inherits the advantages of both the Bamboo Dragonfly and underwater AUVs. The proposed TDAV has a coaxial counter-propeller-tilting platform which satisfies the power and small diameter fuselage requirements, and the blades fold to reduce underwater drag and facilitate transportation. Further, a stable and efficient trans-domain attitude adjustment system is presented which effectively realizes trans-domain attitude switching. Based on the characteristics of the symmetric TDAV body, a rudder blade allocation algorithm is proposed to realize free movement in water. Finally, an improved particle swarm optimization algorithm is used to obtain suitable hierarchical fractional-order PID parameters. Both simulation and outdoor tests were performed and the results demonstrate that the proposed TDAV achieves outstanding performance in terms of lift altitude, trans-domain attitude switching time, and free trans-domain movement in both water and air.
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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 | 0.001 | 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".