Cooperative Motion Mechanism of a Bionic Sailfish Robot With High Motion Performance
Bibliographic record
Abstract
The sailfish possesses outstanding motion performance among marine species, while the robotic fish using the sailfish as a bionic object has received little attention. In this paper, the shape structure and motion characteristics of sailfish are observed, and a bionic sailfish robot is designed that can perform cooperative motion through the dorsal and caudal fins. A novel bionic motion rhythm is developed based on the tail motion characteristics of sailfish, and its propulsive performance is evaluated using experimentation and simulation. The mechanism of cooperative motion between the dorsal and caudal fins is explored from the vortex's perspective, and it is concluded that the vortex formed by the dorsal fin can enhance the propulsive performance of the caudal fin. The results reveal that the phase difference between the dorsal and caudal fins during cooperative motion has a significant effect on the forward speed of the bionic sailfish robot. The average speed of the robotic fish is up to 1.24 m/s when the phase difference is 180°. The turning radius is minimized when the dorsal and caudal fins cooperate in the motion, and a highly maneuverable motion mode is achieved. A high motion performance has been shown by the robotic fish from the perspectives of motion speed, turning performance, and high maneuverability motion mode
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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 | 0.000 |
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".