Bibliographic record
Abstract
The study of biomimetic robotic fish has received a growing amount of research interest in the past several years. This thesis describes the development and testing of a novel mechanical design of a biomimetic robotic fish. The robotic fish has a structure which uses oscillating caudal fins and a pair of pectoral fins to generate fish-like swimming motion. This unique design enables the robotic fish to swim in two swimming modes, namely Body/Caudal Fin (BCF) and Median/Paired Fin (MPF). In order to combine BCF mode with MPF mode, the robotic fish utilizes a flexible posterior body, an oscillating foil actuated by three servomotors, and one pair of pectoral fins individually driven by four servomotors. Effective servo motions and swimming gaits are then proposed to control its swimming behaviour. Based on these results, fish-like swimming can be achieved including forward, backward, and turning motions. An experimental setup for the robotic fish was implemented using machine vision position and velocity measurement. The experimental results show that the robotic fish performed well in terms of manoeuvrability and cruise speed. Based on the experimental data, a low order dynamic model is proposed and identified. Together, these results provide an experimental framework for development of new modelling and control techniques for biomimetic robotic fish.
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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) | 0.002 | 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".