A Hexagonal Grid Cell-inspired Collision Avoidance Motion Planning Algorithm for USVs in a Velocity Domain-based Marine Environment
Bibliographic record
Abstract
Optimal motion planning is a challenging problem for unmanned surface vehicle (USV) systems, especially in complex ocean environments, with inevitable dynamic obstacles induced by currents or wind flows. In this work, we propose a new motion planning algorithm for USVs considering flowing obstacles, path distance and time consumption optimization, inspired by neuron structure of entorhinal grid cells in mammals' brains, and how these grid cells learn and build cognitive maps. The proposed collision avoidance motion planning algorithm comprises three key components. First, a hexagonal grid cell-based neural network is designed to provide references of paths with optimal distance for vehicles, where one of the six neurons surrounding the vehicle's current position will be selected due to their residual lengths to the target. Second, a velocity domain that collects the kinematics of environmental flows, dynamic obstacles and the vehicle is constructed. The optimal reciprocal collision avoidance (ORCA) method is then applied to obtain the most efficient collision avoidance velocity vector for the USV, and an adjustment velocity component can be generated to eliminate both flow effects and collisions. The effectiveness of the proposed approach is demonstrated through extensive simulations in 2D conditions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".