A Fault-Tolerant Fuzzy-Logic Based Redundancy Resolution Method for Underwater Mobile Manipulators
Bibliographic record
Abstract
In this work, a fault-tolerant redundancy resolution scheme is presented that allows a single 6-DOF command to be distributed over a small URVM system composed of an otherwise underactuated URV and serial manipulator. The URVM system admits an infinite number of joint-space solutions for each commanded end-effector state due to its inherent redundancy. The primary objective is realized using the right Moore-Penrose pseudoinverse solution. The secondary objectives are: avoiding manipulator joint limits, avoiding singularity and high joint velocity; keeping the end-effector in sight of the on-board camera minimizing the URV motion; and minimizing the drag-force resistance, or weathervaning. Each criterion is defined within the framework of the Gradient Projection Method. The hierarchy for the secondary tasks is established by a low-level artificial pilot that determines a weighting factor for each criterion based on if- then type fuzzy rules that reflect an expert human pilot's knowledge. A Mamdani fuzzy inference system is used to interpret the fuzzy rules based on the sensory knowledge. The resulting weight schedule yields a self-motion (null-space motion) that emulates how a skilled operator would utilize the full capabilities of the URVM to achieve the secondary objectives. The proposed redundancy resolution scheme has a fault-tolerant property. When a joint failure occurs, the scheme automatically redistributes the end-effector velocity command taking into account the faulty joints. To demonstrate the efficacy of the proposed scheme, several numerical simulations are performed The results illustrate the validity of the proposed redundancy 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".