Development of a coordinated controller for underwater vehicle-manipulator systems
Bibliographic record
Abstract
In this work, the motions of an underwater remotely operated vehicle (ROV) and a spatial manipulator are coordinated using a consolidated controller. The controller translates a single pilot command, the desired position and orientation of the end effector, into a coordinated set of ROV and manipulator joint motions that satisfy the pilot intent in addition to a series of 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 novel sliding-mode based controller is proposed. The controller contains two layers of adaptivity. The first layer is for adjusting PID gains, whereas the second layer is for estimating the bound on a lumped uncertainty vector. The second level of adaptation is shown to relax the Lyapunov stability requirements leading to a more robust controller. To generate reference state values, a redundancy resolution technique is utilized that is based on the gradient projection method merged with a fuzzy determination of the hierarchy of the secondary objectives. The redundancy resolution method distributes the pilot's end-effector command over the ROV and the manipulator in an optimal manner using the redundant degrees of freedom. The results illustrate that detailed subsea tasks can be completed with a small, low-cost ROVM system using the proposed unified control 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".