Optimizing unmanned surface vehicle control: A data-enabled learning approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned surface vehicles (USVs) have gained significant attention recently for applications such as delivery and trash removal. However, accurately modeling these vehicles is difficult due to their inherent underactuation and complex dynamics, which often result in inaccurate tracking. To address this challenge, we propose a data-enabled learning approach to fully exploit the abundant data available for achieving enhanced control performance. The core concept is that suboptimal motion generates a substantial amount of data, specifically related to surge, yaw rate, and control inputs. This rich information can enable an efficient learning process to enhance motion control. In this work, we use data collected from experiments to optimize planar motion control in an underactuated vessel. The optimization algorithm allows for efficient tuning of the control gains for a predefined controller, with quick convergence. Importantly, the gain optimization does not require knowledge of the vehicle model. Simulations and experiments conducted on a vessel prototype demonstrate improved controller performance and efficiency in learning.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it