Multiunmanned Aerial Vehicle Path Planner on Graphics Processing Unit
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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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