Design and development of autonomous amphibious unmanned aerial vehicle for in situ water quality assessment and water sampling
Bibliographic record
Abstract
This paper presents the design and development of an autonomous amphibious unmanned aerial vehicle (AAUAV) system with hybrid version of a multicopter and a hovercraft that can vertically take-off and land as well as travel on water and smooth earth surfaces for water-based applications like water quality assessment, water sampling, remote sensing, underwater mapping, etc. Based on the conceptual design, parameters such as aircraft overall dimensions, weight estimation, aircraft performance, power requirement, and endurance of the UAV were calculated and evaluated. Using these computed parameters, a suitable propulsion system was selected. A 3D CAD model of the UAV was developed, and 2D manufacturing drawings were made. Finally, a prototype of the UAV was fabricated, assembled, and all the sub-systems were integrated. Initial trial runs were made to check the proper functioning of all sub-systems as intended. Subsequently, to verify and validate the UAV’s concept and design configuration, ground and field tests were conducted to test the vertical take-off and landing, flying, and amphibious capabilities of the developed unmanned system. Results of the tests have proven that a well-conceived design and development has been successfully completed.
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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".