Comprehensive underwater vehicle-manipulator system teleoperation
Bibliographic record
Abstract
In this work, a novel comprehensive scheme for the coordinated control of remotely operated vehicle-manipulator systems (ROVMs) is proposed. In the proposed scheme, instead of commanding the motion of the vehicle and the manipulator separately, a human pilot commands only the manipulator's end-effector motion using a parallel- architectured six-degree-of-freedom (6-DOF) joystick. The generated reference motion is then converted into a set of desired ROV and manipulator joint motion by means of using a redundancy resolution scheme that provides the means to utilize redundant degrees of freedom to accomplish secondary objectives. The redundancy resolver uses the Gradient Projection Method combined with a Mamdani-based fuzzy determination of the hierarchy of the secondary objectives. The controller relies on a unified dynamic model of the system. The quasi-Lagrange method is used to derive the equations of motion in terms of the ROV body-fixed frame. For the control problem, a sliding-mode based controller is used that contains an adaptive term for the estimation of the upper bound on the lumped uncertainty vector. The hardware-in-the-loop simulation studies illustrate that detailed subsea tasks can be completed with a small, low-cost ROVM system using the proposed ROVM operation scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".