A Hybrid Path Planning Strategy of Autonomous Underwater Vehicles
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
Autonomous Underwater Vehicles (AUVs) play a unique role in many applications, including oceanographic research, country defense, ecosystem monitoring, to name a few. The autonomy of AUVs is utilized in automatically planning a feasible path/trajectory to a goal point. A robust planner of AUVs should be able to search a collision-free path/trajectory not only in a large-scale known static environment, but also in the environment with dynamic obstacles. This paper demonstrates a modified and combined Dynamic Window Approach (DWA) and Rapidly-exploring Random Tree (RRT*) to plan both the local trajectory and the global path for AUVs in environments where dynamic obstacles may appear. In case of dynamic obstacles, the planner automatically judges the risk of collision and switches from RRT* to DWA if necessary. Then the planner switches back after collision risk is dismissed. Hence, by switching between two algorithms, the balance of real-time computation and the globally optimal solution is achieved. The effectiveness of the proposed hybrid planning strategy is verified by simulation.
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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 | 0.001 | 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 it