Elastostatic analysis of a module-based shape morphing snake-like robot
Bibliographic record
Abstract
This paper describes the stiffness analysis of a module-based shape morphing snake-like robot. Snake-like robots have the characteristic of adapting to unstructured environments by exploiting their ability to reconfigure their body’s shape. However, the excellent mobility contrasts with the ability to transmit high loads, precluding its application in manufacturing operations. This article presents a hybrid structure based on reconfigurable modules equipped with lockable joints. The use of multiple modules in series allows for a large workspace. Furthermore, the parallel structure of the single modules provides for transferring or sustaining high loads. First, the reliability and precision of the theoretical model has been verified using finite element analysis (FEA). The relative errors are less than 5%. Then, a morphing module has been constructed as a physical demonstrator for the kinematic parameters and stiffness parameters used in elastostatic analysis. Finally, a five-segment prototype has been manufactured and tested. There is a deviation between the experimental results and the theoretical results due to manufacturing errors of the prototype but the trend of displacement change shown in the experimental results is basically consistent with the theoretical results.
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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".