Remote Operation of Marine Robotic Systems and Next-Generation Multi-Purpose Control Rooms
Bibliographic record
Abstract
Since 2017, NTNU’s Applied Underwater Robotics Laboratory has been developing an infrastructure for remote marine/subsea operations in Trondheim Fjord. The infrastructure, named the OceanLab subsea node, allows remote experimentation for three groups of assets: seabed infrastructure, surface or subsea vehicles/robots, and assets at remote experimentation sites. To achieve this task, a shoreside control room serves as a hub that enables efficient and diverse communication with assets in the field as well as with remote participants/operators. Remote experimentation has become more popular in recent years due to technological developments and convenience, the COVID-19 pandemic, and travel restrictions that were imposed. This situation has shown us that physical presence at the experimentation site is not necessarily the only option. Sharing of the infrastructure among different experts, which are geographically distributed, but participating in a single, local, real-time experiment, increases the level of expertise available and the efficiency of the operations. This paper also elaborates on the development of a virtual experimentation environment that includes simulators and digital twins of various marine vehicles, infrastructures, and the operational marine environment. By leveraging remote and virtual experimentation technologies, users and experts can achieve relevant results in a shorter time frame and at a reduced cost.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".