DYNAMICS OF TWO ACTIVE AUTONOMOUS DOCK MECHANISMS FOR AUV RECOVERY
Bibliographic record
Abstract
Autonomous Underwater Vehicles (AUVs) are presenting an ever expanding range of applications that enhance human capabilities and mitigate human risk. Development of a successful subsurface autonomous launch and recovery system would expand the functional use of AUVs in many fields, e.g., year-round Canadian Arctic exploration and sovereignty missions. This paper provides an overview of the design and dynamic modelling of two concept mechanisms being developed to recover AUVs to a slowly moving submerged submarine. Both have a serial R⊥R⊥P architecture; one is mechanically actuated while the second uses an actively pitched wing to indirectly provide motive force for the passive revolute joint. Dynamic models of both manipulators are developed. Although similar in architecture, several extensions are required to accurately predict the non-linear dynamics provided by the wing. High speed actuation of the devices is required to compensate for relative trajectory errors between the submarine and AUV during significant sea states in littoral waters. Alterations to the recursive Newton–Euler method to include hydrodynamic and additional inertial forces present in water are explained. Results of some initial modelling are presented.
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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 | 0.000 |
| 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 | 0.001 |
| 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".