Droplet: Towards Autonomous Underwater Assembly of Modular Structures
Bibliographic record
Abstract
This paper presents a first low-cost autonomous robotic system for underwater assembly of mortarless structures. The long-term goal is to enable the construction of large-scale underwater structures, such as retaining walls and artificial reefs. The approach follows the principle of co-design; the 2-DOF manipulator and blocks are designed to complement the localization and control strategies. The blocks and gripper are designed with a connector geometry that removes error during pickup of blocks and drop assembly. This error correction feature allows a simplification of localization and control, which are based on fiducial markers on custom platforms. We developed the proposed system on a low-cost heavily modified BlueROV2 autonomous vehicle — which we call Droplet — with a two-degree of freedom hand that can open and close a gripper and rotate over the yaw. We performed extensive experiments in the pool to evaluate each component and the system as a whole. Results showed a 100% success rate in dropping blocks in the presence of some localization and control errors and the assembly of several different 3D structures composed of up to eight blocks.
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 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".