A GPU Accelerated Path Planner for Multiple Unmanned Aerial Vehicles
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV's) have experienced an increased usage in the execution of surveillance and reconnaissance tasks, primary reasons being their versatility, low cost, elimination of human risk, and potential autonomous capabilities. This task requires the aircraft to overfly specified points of interest in an efficient manner whilst avoiding terrain and dangerous regions. To accomplish this autonomously, a path planning module capable of calculating and determining the most appropriate route must be implemented. It must be capable of providing a solution in a robust and timely manner to allow for live flight path updating. This paper proposes a flight planner for a reconnaissance scenario in which multiple UAV's are required to overfly numerous points of interest (POI) in a given geographical area. The approach in this paper is presented as a three step solution; the set up and formatting of input data, solving the single source shortest point problem for each POI using Bellman Ford, and the distribution and assignment of the appropriate path for each UAV using the Genetic Algorithm. It was shown that the acceleration of this process, achieved by using a Graphics Processing Unit (GPU) allowed for an average speed-up of 11x allowing for rapid path planning.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".