Exploiting Redundancy In Underwater Vehicle-Manipulator Systems
Bibliographic record
Abstract
The current work focuses on the development of a comprehensive scheme for the coordinated control of remotely operated vehicle–manipulator (ROVM) systems, and it proposes a novel mode for their operation. The proposed scheme consists of 2 main stages: redundancy resolution, and and robust model based control. In the redundancy resolution stage, the end-effector input commanded by a human pilot is distributed over the vehicle and manipulator. The redundant degrees of freedom (DOF) are used to accomplish secondary objectives. To this end, the Gradient Projection Method (GPM) is merged with a fuzzy logic based weighting scheme. Regarding the control of the system, a dynamic model is derived using the energy-based quasi–Lagrange approach. As opposed to a classic Lagrangian derivation, the quasi–Lagrange approach generates the equations in terms of body-fixed frames. As well, an adaptive sliding mode controller is implemented that constantly compensates for unknown dynamics throughout the vehicle-manipulator system. To demonstrate the efficacy of the scheme, a numerical case study is performed. Results illustrate that a complex end-effector spatial maneuver defined by a single 6-DOF pilot input can be accomplished with a 4-DOF manipulator mounted on a small ROV.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".