Development of a Low-Cost Soft Robot Fish With Biomimetic Swimming Performance
Bibliographic record
Abstract
Abstract This paper investigates the methodology and techniques for a soft biomimetic robot fish that has a straightforward design, relatively simple fabrication, and low cost. In addition to the investigations of fabrication techniques, we also explore the numerical analysis of the biological fish swimming performance, with its inspiration for robot fish design, which is less studied in the literature. In this research, therefore, various swimming locomotion patterns within the body and/or caudal fin family are analyzed for kinematics and hydrodynamics using analytical methods and computational fluid dynamics (CFD) to inspire the robot fish design for improved swimming performance. By straightforward design and fabrication, the swimming performance of the numerical robot fish is verified by means of simulation using 3D CFD, and the prototype performance is validated using in-water experimental tests. This study showcases a new easy-to-design and easy-to-fabricate robust biomimetic robot fish with comparable swimming performance, which has good potential for purposes like education, research, and entertainment.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".