Design of Small Unmanned Surface Vehicle with Autonomous Navigation System
Bibliographic record
Abstract
Maintaining ecosystems is one of the current leading public concerns. Some of the pressing problems that threaten water resources are pollution of water bodies with floating debris, illegal extraction of water resources, wear and tear of underwater communications. Therefore, the creation of special technical solutions is urgent. This paper reports a model-based design of an unmanned surface vehicle (USV), purposed to control and maintain the oxygen level and parameters such as acidity and the water temperature in rivers, lakes, inland waterways, and coastal waters. The developed USV navigation autopilot is described as a system with two inputs and one output. The autopilot selected is an adaptive controller based on the concept of proportional, integral, and derivative (PID). The autopilot is implemented on the STM32 microcontroller and allows precisely maintaining a given course, adjusting the speed and angle of rotation during wind drift and other influences. The new technique for sensor calibration and data acquisition is described. Simulation results are given, showing the performance of the autopilot algorithm in response to variations in the environment. The numerical experiments of the model have presented the result of confirming the sufficient correspondence between prototype characteristics and simulation results. Finally, thorough field trials were performed to verify the reliability and precision of the proposed solutions. The developed unmanned surface vehicle can be used for environmental monitoring (water sampling, hydrography, patrolling water areas). In turn, the solutions obtained will be suitable for the design of other USV of different sizes.
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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) | 0.002 | 0.001 |
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".