Continuous Collision Detection of Cubic-Spline-Based Tethers in ROV Simulations
Bibliographic record
Abstract
Improving the efficacy of the pilot remotely operated vehicle (ROV) interaction through extensive training is paramount in reducing the duration, and thus expense, of ROV deployments. To complete training without sacrificing operational windows, ROV simulators can be used. Since the ROV tether, which provides power and telemetry, will at times dominate the ROV motion, the tether must be accurately modeled over the full duration of a simulated ROV maneuver. One aspect of the tether dynamics that remains relatively untouched is the modeling of tether self-contact, contact with other tethers, or entanglement. The aim of this work is to present a computationally efficient and accurate method of detecting tether collisions. To this end, a combinatorial global optimization method is first used to determine the approximate separation distance minima locations. Then, a local optimization scheme is used to find the exact separation distances and the locations of the closest points. The first combinatorial stage increases the speed at which the minima can be found. The minimum separation distance information and its change with respect to time can then be used to continuously determine whether a collision has occurred. If a collision is detected, a contact force is calculated from the interference geometry and applied at the collision site.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".