Modular Foldable Airship Concept for Subterranean Exploration
Bibliographic record
Abstract
Abstract The exploration of new lands has always been a source of motivation for mankind. Despite the common idea that our planet is fully known, a huge number of inaccessible places still remains unvisited today, especially below the surface. Recent advances in robotics allow some of these locations to be explored by unmanned vehicles. This paper presents the design of a 3-modules lighter-than-air vehicle specifically conceived to autonomously explore inaccessible caves and underground environments. The design is inspired from an arthropod, scutigera coleoptrata, a long-legged centipede commonly found in our houses. Instead of crawling on walls like its biological counterpart, the robotic scutigera hovers and flies in cave tunnels. The aim is to develop a flexible semi-rigid, segmented airship that can withstand long, smooth explorations of caves while transmitting in real-time the images and sounds that it captures. To develop the model of the system, the kinematics of the modules are obtained in the inertial frame, and the dynamic derivation of the vehicle is obtained using Kane’s equations, which can also be extended for an n-bodies system. Its motion is illustrated with a couple of simplified scenarios in the horizontal plane and only having actuation in the front, or first module, of the airship. A structural design of the modules is presented and supported with a proof-of-concept prototype.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".