Bearing-Only-Based Cooperative Target Enclosing Control for Multiple Uncrewed Surface Vehicles With Unknown Dynamics and Sideslip
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
Cooperative target enclosing control for uncrewed surface vehicles (USVs) is critical in tackling complicated maritime tasks in many scenarios. This article proposes a cooperative target enclosing control framework for multi-USV systems, focusing on unknown targets under the constraints of bearing-only measurements, sideslip effects, unknown dynamics, and external disturbances. A bearing-only-based cooperative target estimator is introduced to estimate the relative position and velocity of the unknown target in practical situations where only bearing measurements are available. Cooperative states among neighboring USVs are incorporated to relax the persistent excitation (PE) condition, enhancing the estimator's robustness. A cooperative controller based on USV kinematics is designed to achieve both distance keeping and evenly spaced circumnavigation with neighbors. To account for the sideslip effects caused by the unknown sway velocity of the USVs, extended state observers are employed to estimate and compensate for the unknown kinematic terms involving sway velocity, thereby improving the target enclosing control performance. In addition, a radial basis function neural network-based dynamic controller is developed to approximate and compensate for uncertain nonlinear functions in the dynamics, ensuring the stability of individual USVs in the presence of uncertain dynamics. To address the issue of complexity explosion in online adaptive networks, a minimal learning parameters technique is adopted to reduce the number of weights that need to be online adapted to two, thereby effectively alleviating the computational load. Comparative simulations are implemented to verify the effectiveness of the proposed target enclosing framework for multi-USV systems.
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 | 0.000 | 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 it