Path Planning for Multiple USV Collecting Seabed-based Data Based on UWA Communication
Bibliographic record
Abstract
Seabed-based observation networks (SBONs) provide continuous and efficient monitoring of seafloor conditions. However, collecting data of SBONs is labor-intensive and resource-intensive. In this paper, we built a mathematical model and propose a two-stage path planning algorithm that combines underwater acoustic (UWA) communication using multiple unmanned surface vessels (USVs) to collect data from SBONs. In the first stage, an immune algorithm is innovatively improved to obtain the optimal SBONs access order for each USV in combination with the cost of obstacle avoidance paths between nodes. In the second stage, the proposed estimation solution method (ESM) and dynamic window method are used to solve the proposed constrained optimization problem based on the energy transmission range of UWA communication and obstacle avoidance problem. Experiments show that the proposed algorithm can solve the problem of collecting SBONs data by multiple USVs and can achieve better performance in terms of path length, workloads between USVs and time compared with other methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".