Energy analysis of worm locomotion on flexible surface
Bibliographic record
Abstract
Recent attempts at designing untethered devices for locomotion inside compliant biological vessels, highlighted the requirements for energy efficiency for prolonged duration inside living bodies. Quite a number of studies considered the design and construction of crawling robots but very few focused on the interaction between the robots and the flexible environment. In previous studies, we derived the efficiency, defined as the actual advance divided by the optimal advance, of worm locomotion. In this paper, we analyze the force, minimum energy and power requirements for worm locomotion over flexible surfaces. More importantly, we determine the optimum conditions of locomotion as a function of the number of cells, friction coefficients, stroke length, energy recovery factor, and tangential compliance. Optimality is defined with respect to energy and power requirements. The analytical results are obtained by integrating the force over the actuator motion and alternatively by summing up the overall energy losses due to friction and elastic losses with the surface and the efficient work performed by the robot. The theoretical predictions are compared to numerical simulations modeling worm robots crawling over flexible surfaces and are found in perfect match.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".