Ninja legs: Amphibious one degree of freedom robotic legs
Bibliographic record
Abstract
In this paper we propose a design of a class of robotic legs (known as “Ninja legs”) that enable amphibious operation, both walking and swimming, for use on a class of hexapod robots. Amphibious legs equip the robot with a capability to explore diverse locations in the world encompassing both those that are on the ground as well as underwater. In this paper we work with a hexapod robot of the Aqua vehicle family (based on a body plan first developed by Buehler et al. [1]), which is an amphibious robot that employs legs for amphibious locomotion. Many different leg designs have been previously developed for Aqua-class vehicles, including both robust all-terrain legs for walking, and efficient flippers for swimming. But the walking legs have extremely poor thrust for swimming and the flippers are completely unsuitable for terrestrial operations. In this work we propose a single leg design with the advantages of both the walking legs and the swimming flippers. We design a cage-like circular enclosure for the flippers in order to protect the flippers during terrestrial operations. The enclosing structure also plays the role of the walking legs for terrestrial locomotion. The circular shape of the enclosure, as well, has the advantages of an offset wheel. We evaluate the performance of our design for terrestrial mobility by comparing the power efficiency and the physical speed of the robot equipped with the newly designed legs against that with the walking legs which are semi-circular in shape. The swimming performance is examined by measuring the thrust generated by newly designed legs and comparing the same with the thrust generated by the swimming flippers. In the field, we also verified that these legs are suitable for swimming through moderate surf, walking through the breakers on a beach (and thus through slurry), and onto wet and dry sand.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".