Efficient Path Planning and Dynamic Obstacle Avoidance in Edge for Safe Navigation of USV
Bibliographic record
Abstract
Unmanned surface vessel (USV) has been widely used in various fields due to its autonomous advantages, and path planning is a crucial technology for autonomy. However, using global path planning alone cannot avoid moving obstacles, while using local path planning alone may lead to falling into local minima and fail to reach the target. Therefore, this article proposed the dynamic target artificial potential field (DTAPF) method which use a dynamic point that follows the global path generated by the A* algorithm as the target point of the artificial potential field (APF). In addition, in order to improve response time and safety of unmanned surface vessel (USV) navigation of the traditional centralized path planning methods, we proposed an edge computing architecture for global path planning and an offset guidance method to avoid moving obstacles while confirming to the collision regulations (CORLEGs). The experimental results show that, using the method proposed in this article, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to the traditional APF algorithm, our method can reduce collision probability by 71% with almost no increase in average path length and average navigation time. Besides, our architecture has much lower computing delay than local computing, and also lower than cloud computing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".