Detecting Tether Self-Collisions in Tethered ROV Simulations
Bibliographic record
Abstract
Currently, the only viable means of providing power and maintaining human control during underwater ROV operation is through the vehicle’s tether. Due to the high cost of ROVs and their tethers as well as potential risks to equipment and personnel, a realistic simulator is needed to train their pilots. To accurately simulate the tether, it is important to detect collisions of the tether with the environment and with itself as well as to calculate the forces involved. The aim of this work is to present a computationally efficient and accurate method of detecting tether self-contact. To this end, a combinatorial global optimisation method is first used to determine the approximate separation distances. Then, a local optimisation scheme is used to find the exact separation distance and the location of the closest points. This information can then be used to determine whether or not a collision has occurred. If a collision is detected, a force is applied at the collision site to maintain separation.
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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 | 0.000 | 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 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".