Optimally efficient swimming in hyper-redundant mechanisms: control, design, and energy recovery
Bibliographic record
Abstract
Hyper-redundant mechanisms (HRMs), also known as snake-like robots, are highly adaptable during locomotion on land. Researchers are currently working to extend their capabilities to aquatic environments through biomimetic undulatory propulsion. In addition to increasing the versatility of the system, truly biomimetic swimming could also provide excellent locomotion efficiency. Unfortunately, the complexity of the system precludes the development of a functional solution to achieve this. To explore this problem, a rapid optimization process is used to generate efficient HRM swimming gaits. The low computational cost of the approach allows for multiple optimizations over a broad range of system conditions. By observing how these conditions affect optimal kinematics, a number of new insights are developed regarding undulatory swimming in robotic systems. Two key conditions are varied within the study, swimming speed and energy recovery. It is found that the swimmer mimics the speed control behaviour of natural fish and that energy recovery drastically increases the system's efficiency. Remarkably, this efficiency increase is accompanied by a distinct change in swimming kinematics. With energy recovery, the swimmer converges to a clearly anguilliform gait, without, it tends towards the carangiform 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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".