DESIGN, DEVELOPMENT AND LOCOMOTION CONTROL OF BIO-FISH ROBOT WITH UNDULATING ANAL FINS
Bibliographic record
Abstract
This paper describes the design of the propulsion system and depth control of a robot fish. The design is inspired from the knifefish. The propulsion system is based on the undulating fins. A simplified model of the fins is constructed by using oscillating servomotors to which are attached cranks holding flexible membranes that can slide permitting the variation of the length of the fins. The depth control was inspired from the gas bladder of fish which changes volume to control the depth based on the buoyancy forces. A buoyancy tank, equipped with two pistons, is used to change the submerged volume. Proximity sensors are used to monitor and limit the piston movement. The robot fish was controlled using microprocessors which takes readings from proximity, depth and pitch sensors, and implements a control scheme. Bluetooth technology was used to interface the robot fish to a laptop to allow the operator to control the robot fish. The propulsion speed was measured as a function of phase angle and a frequency of the servomotors. Testing has also been conducted in an outdoor diving pool to demonstrate the capability of the knifefish robot in various swimming courses and also the depth control of the bio-fish 4 m under the water. The design structures and mechanisms can be scaled down for micro-bio-fish models. The micro-fin model driven by a smart actuator is being tested. The bio-mechatronics implementation can be applied in defence and medical applications as the model is modular, scalable, and re-configurable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".