Design of a low-cost unmanned surface vehicle for swarm robotics research in laboratory environments
Bibliographic record
Abstract
Swarm robotics is the study of groups of simple, typically inexpensive agents working collaboratively toward a common goal. Such systems offer several benefits over single-robot solutions: they are flexible, scalable, and robust to the failure of individual agents. The majority of existing work in this field has focused on robots operating in terrestrial environments but the benefits of swarm systems extend to applications in the marine domain as well. The current scarcity of marine robotics platforms suitable for swarm research is detrimental to progress in this field. Of the few that exist, no publicly available unmanned surface vehicles can operate in a laboratory environment; an indoor tank of water where the vessels, temperature, lighting, etc. can be observed and controlled at all times. Laboratory testing is a common intermediate step in the hardware validation of algorithms. This thesis details the design of the microUSV: a small, inexpensive, laboratory-based platform developed to fill this gap. The microUSV system was validated by performing laboratory testing of two algorithms: a waypoint-following controller and orbital retrieval. The waypoint-following controller was a simple PI controller implementation which corrects a vessel's speed and heading to seek predetermined goal positions. The orbital retrieval algorithm is a novel method for a swarm of unmanned surface vehicles to gather floating marine contaminants such as plastics. The vessels follow a circular path, orbiting around a central collection location and veer outwards to retrieve contaminants they detect outside the designated area. This method can potentially be used to cluster floating plastics together from a large region to facilitate cleanup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".