Multiunmanned Aerial Vehicle Path Planner on Graphics Processing Unit
Bibliographic record
Abstract
Using multiple unmanned aerial vehicles (UAVs) improves the efficiency of reconnaissance, surveillance, and search and rescue missions. This article presents a path-planning software for a team of UAVs utilizing graphics processing units (GPUs). The UAVs are tasked to visit multiple points of interest (POIs) in a 3-D environment, and the software finds an optimized solution that assigns the POIs to the UAVs, selects the order in which the POIs are visited, and calculates the paths between the POIs. The software uses a multistep approach using a single-source-shortest-path algorithm to find the optimal paths between all combinations of POIs followed by a genetic algorithm to solve the multitraveling salesperson problem. The algorithm can minimize distance, time, or energy consumption depending on the setting selected by the user. The proposed GPU implementation is tested on eight different maps from around the world and executes in just 0.6 s, a 48.3× speedup compared to a sequential execution on CPU. This performance improvement is a real asset in a mission-changing environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.004 | 0.001 |
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".