Intelligent Motion Control to Enhance the Swimming Performance of a Biomimetic Underwater Vehicle Using Reinforcement Learning Approach
Bibliographic record
Abstract
This article develops an intelligent motion control strategy using reinforcement learning to regulate a biomimetic autonomous underwater vehicle (BAUV) swimming performance. The BAUV is driven by the oscillatory motion of a lunate-shaped caudal fin. To navigate effectively, the vehicle must regulate its propeller’s motion to swim toward goals. The caudal fin’s oscillatory motion is defined by sine functions, where the amplitude, frequency, bias, and time shifting are the main flapping parameters that must be regulated. The developed algorithm, based on the n-step state-action-reward-state-action on-policy learning, fine-tunes these parameters. Further, deep Q learning network speed adjusters were designed to enhance BAUV guidance. By integrating waypoint guidance systems and intelligent path trackers, the vehicle reduces its heading deviation and distance to goals. The effectiveness of these methods was validated through simulations featuring various disturbances like longitudinal, lateral, and swirl currents. The proposed scheme allowed the vehicle to reach desired targets efficiently, even in the presence of strong currents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".