Collaboration of Heterogeneous Marine Robots Toward Multidomain Sensing and Situational Awareness on Partially Submerged Targets
Bibliographic record
Abstract
This article reports on a multirobot system that collaboratively obtains above-water, surface, and below-water information on a floating target. This capability allows a ship to autonomously survey and obtain situational awareness on a floating unresponsive target from a safe stand-off before inspecting it more closely or navigating around it. The target could be another ship, structure, or a navigational obstruction like an iceberg. The proposed solution is a collaborative system with an unmanned aerial vehicle (UAV), an unmanned underwater vehicle (UUV), and an unmanned surface vehicle (USV). The UAV captures visual imagery to create a 3-D model of the target’s above-water geometry using photogrammetry. The UUV surveys the target’s submerged hull with integrated imaging and profiling bathymetric sonars. The USV hosts an intelligent mission-planning node which manages the robotic collaboration in a centralized architecture by autonomously planning and distributing the missions for the UUV and UAV. The intelligent node also adaptively plans the USV’s trajectory to support the other autonomous assets, specifically reducing and bounding the UUV’s state-estimate error through collaborative localization. The resulting above- and below-water sensor data is fused at the waterplane, using a sliding correlation algorithm, to yield a 3-D representation of the floating unresponsive target. The contributions from this article include the cross-domain robotic collaboration and autonomous mission-planning toward acquiring and fusing data from heterogeneous robots. The autonomous mission-planning and data-merging algorithms are presented. The setup and results from simulations and in-water testing with an UUV, USV, and UAV are described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".