A Novel Algorithm of Multi-AUVs Task Assignment and Path Planning Based on Biologically Inspired Neural Network Map
Bibliographic record
Abstract
The task assignment and path planning of a multi-AUVs system has now attracted considerable attention and become a hotspot in the research. In this paper, a novel algorithm of multi-AUVs task assignment and path planning based on Biologically Inspired Neural Network Map (BINN) is proposed. Firstly, the grid map is built by discretizing the three-dimensional underwater environment into many equal grids. Secondly, the activity values of all AUVs in the BINN maps of each target are calculated. Then, the AUV with the highest activity value in the BINN map of the target is selected as the winning AUV for the target. Finally, the winning AUV performs path planning according to the BINN strategy. Through the simulation experiment, it is proved that the novel BINN algorithm proposed in this paper can effectively and reasonably distribute multi-AUVs and reduce the overall sailing distance.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".