Conditions for Worm-Robot Locomotion in a Flexible Environment: Theory and Experiments
Bibliographic record
Abstract
Biological vessels are characterized by their substantial compliance and low friction that present a major challenge for crawling robots for minimally invasive medical procedures. Quite a number of studies considered the design and construction of crawling robots; however, very few focused on the interaction between the robots and the flexible environment. In a previous study, we derived the analytical efficiency of worm locomotion as a function of the number of cells, friction coefficients, normal forces, and local (contact) tangential compliance. In this paper, we introduce the structural effects of environment compliance, generalize our previous analysis to include dynamic and static coefficients of friction, determine the conditions of locomotion as function of the external resisting forces, and experimentally validate our previous and newly obtained theoretical results. Our experimental setup consists of worm robot prototypes, flexible interfaces with known compliance and a Vicon motion capture system to measure the robot positioning. Separate experiments were conducted to measure the tangential compliance of the contact interface that is required for computing the analytical efficiency. The validation experiments were performed for both types of compliant conditions, local and structural, and the results are shown to be in clear match with the theoretical predictions. Specifically, the convergence of the tangential deflections to an arithmetic series and the partial and overall loss of locomotion verify the theoretical predictions.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".