Massively parallel hybrid algorithm on embedded graphics processing unit for unmanned aerial vehicle path planning
Bibliographic record
Abstract
To operate autonomously, military unmanned aerial vehicles (UAVs) must be equipped with a path planning module capable of calculating feasible trajectories. This is a highly complex and nonlinear optimisation problem that challenges state of the art methods. In this paper, we present a massively parallel hybrid algorithm to solve the path planning problem for fixed-wing military UAVs. The proposed solution combines the strengths of the genetic algorithm (GA) and the particle swarm optimisation and allows for the calculation of quasi-optimal paths in realistic 3D environments. To reduce the execution time, the proposed algorithm is parallelised on the NVIDIA Jetson TX1 embedded graphics processing unit (GPU). By exploiting the parallel architecture of the GPU, the runtime is reduced by a factor of 23.6× to just 4.3 seconds while requiring only 10 watts, making it an excellent solution for on-board path planning. The proposed system is tested in a simulation using 18 scenarios on six different terrains.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".