A survey of AUV and robot simulators for multi-vehicle operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a survey of a selection of currently available simulation software for robots and unmanned vehicles. In particular, the simulators selected are reviewed for their suitability for the simulation of Autonomous Underwater Vehicles (AUVs), as well as their suitability for the simulation of multi-vehicle operations. The criteria for selection are based on the following features: sufficient physical fidelity to allow modelling of manipulators and end effectors; a programmatic interface, via scripting or middleware; modelling of optical and/or acoustic sensors; adequate documentation; previous use in academic research. A subset of the selected simulators are reviewed in greater detail; these are UWSim, MORSE, and Gazebo. This subset of simulators allow virtual sensors to be simulated, such as GPS, sonar, and multibeam sonar making them suitable for the design and simulation of navigation and mission planning algorithms. We conclude that simulation for underwater vehicles remains a niche problem, but with some additional effort researchers wishing to simulate such vehicles may do so, basing their work on existing software.
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.
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 it