Applied AI in Water Surface Robotics for Lake Sanitation and Sampling
Bibliographic record
Abstract
Low‑cost robotic systems for water sanitation and sampling are often limited by onboard computation and task‑specific designs, rarely addressing both functions simultaneously. This work introduces an intelligent water surface robot that integrates sanitation and sampling capabilities through real‑time mechanical control and sensor data streaming for off‑board analytics. By shifting computation to external servers, the system enables deployment of advanced algorithms without hardware constraints, with results communicated back for immediate robotic action. The design combines lightweight PVC structures that support up to 17 kg, electronics for cooling, battery monitoring, and sensor integration, as well as APIs for remote connectivity, mapping, and communication. Networking incorporates RTK localisation for precise positioning. Experiments demonstrate the performance of a genetic algorithm-based wayfinding approach with RTK localisation, as well as the deep learning-based floating trash detection from live video streams. This open robotic platform exemplifies applied intelligent systems, enabling robotics engineers to enhance hardware while AI researchers test and refine automation and vision models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".