Biomimetic Soft Robotic Jellyfish for Lentic Ecosystem Monitoring
Bibliographic record
Abstract
Environmental monitoring of lentic ecosystems is well suited for swarm robotics applications since they can operate autonomously and cover large regions. For such a task, a locomotion mechanism is necessary for traversal. Due to the challenging nature of underwater environments, researchers often draw inspiration from nature to improve robot mechanical performance. A box jellyfish in a good inspiration due to their efficient jet propulsion. Soft and smart materials are helpful in mimicking their propulsion because they can perform flexible and complex movement. Shape memory alloy (SMA) springs have been used for this purpose, but their slow actuation rate is a well-known disadvantage. This project aims to improve cycle rate and thus swim speed by implementing a bistable spring configuration into a soft robotic jellyfish. The bistable mechanism stores energy slowly in the expansion of the elastic silicone bell, then releases it suddenly in a contraction which propels the robot forwards with a higher force than would be possible with direct SMA actuation. Additionally, the antagonistic arrangement of SMA springs reset each other and produce the same outward expansion on both up and down stroke, improving cycles speeds by removing the need for a dedicated reset stroke which is typically required before using the springs again. Also, increasing the number of spring pairs does not interfere with the actuation, meaning cycle rates can be further improved by cycling which pair of springs is active. The proof-of-concept device operates at a frequency of 0.4 Hz, and produces enough thrust while tethered to swim through a tank. Future work involves performing fluid visualization tests to validate a simple mathematical model of flow through the bell, as well as optimize the design, integrate onboard environment sensors and implement autonomy to allow operation in a larger swarm.
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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 | 0.001 | 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".