Optimal Motion Planning for Heterogeneous Multi-USV Systems Using Hexagonal Grid-Based Neural Networks and Parallelogram Law Under Ocean Currents
Bibliographic record
Abstract
This article addresses the challenge of enhancing collaboration efficiency within a heterogeneous system of multiple unmanned surface vehicles (USVs) while accounting for the impact of ocean currents. In this context, this article introduces an intelligent algorithm called the hexagonal grid-based neural network with parallelogram law (HGNNPL). The algorithm comprises three key components: 1) a bio-inspired neural network (BINN) designed to predict an optimal collision-free path for a multi-USV system, which operates based on hexagonal partitioning grids, ensuring smooth navigation without collisions; 2) an adjustment component plays a crucial role in correcting deviations caused by ocean currents and calculating the associated energy consumption; and 3) an optimal task assignment component responsible for assigning task objectives to the USVs, where distance determined by the BINN and the energy consumption are involved as motion planning costs. This article presents simulation results that compare the performance of the proposed algorithm with an existing algorithm based on square grids, which does not account for the elimination of ocean current effects. These results illustrate the practical effectiveness of the proposed method.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".